Face recognition is an important part of the broader biometric security systems research. In the past, researchers have explored either the Feature Space or the Classifier Space at a time to achieve efficient face recognition. In this work, both the Feature Space optimization as well as the Classifier Space optimization have been used to achieve improved results. The efficient technique of Feature Fusion in the Feature Space and Classifier Ensemble technique in the Classifier Space have been used to achieve robust and efficient face recognition. In the Feature Space, the Discrete Wavelet Transform (DWT) and the Histogram of Oriented Gradient (HOG) features have been extracted from face images and these have been used for classification purposes after Feature Fusion using the Principal Component Analysis (PCA). In the Classifier Space, a Classifier Ensemble has been used, utilizing the bagging technique for ensemble training, instead of a single classifier for efficient classification. Proper selections of various parameters of the DWT, HOG features and the Classification Ensemble have been considered to achieve optimum performance. The proposed classification technique has been applied to the AT&T (ORL) and Yale benchmark face recognition databases, and we have achieved excellent results of 99.78% and 97.72% classification accuracy respectively. The proposed Feature Fusion and Classifier Ensemble technique has been subjected to sensitivity analysis and it has been found to be robust under reduced spatial resolution conditions.
An Efficient Face Recognition Using Multi-Kernel Based Scale Invariant Featur...CSCJournals
Face recognition has gained significant attention in research community due to its wide range of commercial and law enforcement applications. Due to the developments in the past few decades, in the current scenario, face recognition is employing advanced feature identification techniques and matching methods. In spite of vast research done, face recognition still remains an open problem due to the challenges posed by illumination, occlusions, pose variation, scaling, etc. This paper is aimed at proposing a face recognition technique with high accuracy. It focuses on face recognition based on improved SIFT algorithm. In the proposed approach, the face features are extracted using a novel multi-kernel function (MKF) based SIFT technique. The classification is done using SVM classifier. Experimental results shows the superiority of the proposed algorithm over the SIFT technique. Evaluation of the proposed approach is done on CVL face database and experimental results shows that the proposed approach has a recognition rate of 99%.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
Facial expression analysis is a remarkable and demanding problem, and impacts significant applications in various fields like human-computer interaction and data-driven animation. Developing an efficient facial representation from the original face images is a crucial step for achieving facial expression recognition. Facial representation based on statistical local features, Local Binary Patterns (LBP) is practically assessed. Several machine learning techniques were thoroughly observed on various databases. LBP features- which are effectual and competent for facial expression recognition are generally used by researchers Cohn Kanade is the database for present work and the programming language used is MATLAB. Firstly, face area is divided in small regions, by which histograms, Local Binary Patterns (LBP) are extracted and then concatenated into single feature vector. This feature vector outlines a well-organized representation of face and is helpful in determining the resemblance among images.
Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters IJERA Editor
The Biometrics is used to recognize a person effectively compared to traditional methods of identification. In this paper, we propose a Face recognition based on Single Tree Wavelet Transform (STWT) and Dual Tree Complex Wavelet transform (DTCWT). The Face Images are preprocessed to enhance quality of the image and resize. DTCWT and STWT are applied on face images to extract features. The Euclidian distance is used to compare features of database image with test face images to compute performance parameters. The performance of STWT is compared with DTCWT. It is observed that the DTCWT gives better results compared to STWT technique.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
Age Invariant Face Recognition using Convolutional Neural Network IJECEIAES
In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition. Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH (Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH (Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier.
An Assimilated Face Recognition System with effective Gender Recognition RateIRJET Journal
This document summarizes an assimilated face recognition system that can also perform gender recognition. The system conducts experiments using databases like GENDER-FERET and Cambridge AT&T. For face recognition, it uses the Eigenfaces algorithm to extract features and classify faces. For gender recognition, it uses a trainable COSFIRE filter with Gabor filters to obtain face descriptors, which are classified using an SVM classifier. The experiments achieve a gender recognition rate of over 90%. The paper shows that the gender recognition approach outperforms other methods using handcrafted features and raw pixels.
Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET TransformIRJET Journal
This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.
Face Recognition for Human Identification using BRISK Feature and Normal Dist...ijtsrd
Face recognition is a kind of automatic human identification from face images has been performed widely research in image processing and machine learning. Face image, facial information of the person is presented and unique information for each person even two person possessed the same face. We propose a methodology for automatic human classification based on Binary Robust Invariant Scalable Keypoints BRISK feature of face images and the normal distribution model. In our proposed methodology, the normal distribution model is used to represent the statistical information of face image as a global feature. The human name is the output of the system according to the input face image. Our proposed feature is applied with Artificial Neural Networks to recognize face for human identification. The proposed feature is extracted from the face image of the Extended Yale Face Database B to perform human identification and highlight the properties of the proposed feature. Khin Mar Thi "Face Recognition for Human Identification using BRISK Feature and Normal Distribution Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26589.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/26589/face-recognition-for-human-identification-using-brisk-feature-and-normal-distribution-model/khin-mar-thi
An Efficient Face Recognition Using Multi-Kernel Based Scale Invariant Featur...CSCJournals
Face recognition has gained significant attention in research community due to its wide range of commercial and law enforcement applications. Due to the developments in the past few decades, in the current scenario, face recognition is employing advanced feature identification techniques and matching methods. In spite of vast research done, face recognition still remains an open problem due to the challenges posed by illumination, occlusions, pose variation, scaling, etc. This paper is aimed at proposing a face recognition technique with high accuracy. It focuses on face recognition based on improved SIFT algorithm. In the proposed approach, the face features are extracted using a novel multi-kernel function (MKF) based SIFT technique. The classification is done using SVM classifier. Experimental results shows the superiority of the proposed algorithm over the SIFT technique. Evaluation of the proposed approach is done on CVL face database and experimental results shows that the proposed approach has a recognition rate of 99%.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
Facial expression analysis is a remarkable and demanding problem, and impacts significant applications in various fields like human-computer interaction and data-driven animation. Developing an efficient facial representation from the original face images is a crucial step for achieving facial expression recognition. Facial representation based on statistical local features, Local Binary Patterns (LBP) is practically assessed. Several machine learning techniques were thoroughly observed on various databases. LBP features- which are effectual and competent for facial expression recognition are generally used by researchers Cohn Kanade is the database for present work and the programming language used is MATLAB. Firstly, face area is divided in small regions, by which histograms, Local Binary Patterns (LBP) are extracted and then concatenated into single feature vector. This feature vector outlines a well-organized representation of face and is helpful in determining the resemblance among images.
Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters IJERA Editor
The Biometrics is used to recognize a person effectively compared to traditional methods of identification. In this paper, we propose a Face recognition based on Single Tree Wavelet Transform (STWT) and Dual Tree Complex Wavelet transform (DTCWT). The Face Images are preprocessed to enhance quality of the image and resize. DTCWT and STWT are applied on face images to extract features. The Euclidian distance is used to compare features of database image with test face images to compute performance parameters. The performance of STWT is compared with DTCWT. It is observed that the DTCWT gives better results compared to STWT technique.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
Age Invariant Face Recognition using Convolutional Neural Network IJECEIAES
In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition. Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH (Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH (Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier.
An Assimilated Face Recognition System with effective Gender Recognition RateIRJET Journal
This document summarizes an assimilated face recognition system that can also perform gender recognition. The system conducts experiments using databases like GENDER-FERET and Cambridge AT&T. For face recognition, it uses the Eigenfaces algorithm to extract features and classify faces. For gender recognition, it uses a trainable COSFIRE filter with Gabor filters to obtain face descriptors, which are classified using an SVM classifier. The experiments achieve a gender recognition rate of over 90%. The paper shows that the gender recognition approach outperforms other methods using handcrafted features and raw pixels.
Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET TransformIRJET Journal
This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.
Face Recognition for Human Identification using BRISK Feature and Normal Dist...ijtsrd
Face recognition is a kind of automatic human identification from face images has been performed widely research in image processing and machine learning. Face image, facial information of the person is presented and unique information for each person even two person possessed the same face. We propose a methodology for automatic human classification based on Binary Robust Invariant Scalable Keypoints BRISK feature of face images and the normal distribution model. In our proposed methodology, the normal distribution model is used to represent the statistical information of face image as a global feature. The human name is the output of the system according to the input face image. Our proposed feature is applied with Artificial Neural Networks to recognize face for human identification. The proposed feature is extracted from the face image of the Extended Yale Face Database B to perform human identification and highlight the properties of the proposed feature. Khin Mar Thi "Face Recognition for Human Identification using BRISK Feature and Normal Distribution Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26589.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/26589/face-recognition-for-human-identification-using-brisk-feature-and-normal-distribution-model/khin-mar-thi
Implementation of features dynamic tracking filter to tracing pupilssipij
The objective of this paper is to show the implementation of an artificial vision filter capable of tracking the
pupils of a person in a video sequence. There are several algorithms that can achieve this objective, for this
case, features dynamic tracking selected, which is a method that traces patterns between each frame that
form a video scene, this type of processing offers the advantage of eliminating the problems of occlusion
patterns of interest. The implementation was tested on a base of videos of people with different physical
characteristics of the eyes. An additional goal is to obtain information of the eye movements that are
captured and pupil coordinates for each of these movements. These data could help some studies related to
eye health.
Learning in content based image retrieval a revieweSAT Journals
Abstract
Relevance feedback in Content Based Image Retrieval is an interactive process where the user provides feedback on the systemretrieved
images to bridge the gap between user semantics at high level and machine extracted low level features of images.
RF exploits Machine Learning and Pattern Recognition techniques for Short Term Learning and Long Term Learning to provide
improved performance in retrieval. Intra query and across query learning have received enormous attention over the past
decade. This paper first categorizes the various learning techniques and discusses the intuition behind each of these techniques.
State-of-art learning techniques ranging from Feature Relevance learning to manifold learning in STL and Latent Semantic
Analysis used in text processing to recent kernel semantic learning in LTL are discussed.
Keywords: Relevance Feedback, Short Term Learning, Long Term Learning, Sematic Gap, High Level Features
3D Dynamic Facial Sequences Analsysis for face recognition and emotion detectionTaleb ALASHKAR
This document outlines the PhD thesis of Taleb ALASHKAR on 3D dynamic facial sequence analysis for face recognition and emotion detection. The thesis proposes frameworks for 4D face recognition and 4D spontaneous emotion detection using subspace representations of 3D facial sequences and modeling trajectories on the Grassmann manifold. Experimental results on public databases show the frameworks achieve better recognition and detection performance than state-of-the-art methods, especially in expression-independent face recognition and early detection of emotions like happiness and pain.
Facial Expression Detection for video sequences using local feature extractio...sipij
Facial expression image analysis can either be in the form of
static image analysis or dynamic temporal 3D image or video analysis.
The former involves static images taken on an individual at a specific
point in time and is in 2-dimensional format. The latter involves dynamic textures extraction of video sequences extended in a temporal
domain. Dynamic texture analysis involves short term facial expression
movements in 3D in a temporal or spatial domain. Two feature extraction algorithms are used in 3D facial expression analysis namely holistic
and local algorithms. Holistic algorithms analyze the whole face whilst
the local algorithms analyze a facial image in small components namely
nose, mouth, cheek and forehead. The paper uses a popular local feature
extraction algorithm called LBP-TOP, dynamic image features based on
video sequences in a temporal domain. Volume Local Binary Patterns
combine texture, motion and appearance. VLBP and LBP-TOP outperformed other approaches by including local facial feature extraction
algorithms which are resistant to gray-scale modifications and computation. It is also crucial to note that these emotions being natural reactions, recognition of feature selection and edge detection from the video
sequences can increase accuracy and reduce the error rate. This can be
achieved by removing unimportant information from the facial images.
The results showed better percentage recognition rate by using local facial extraction algorithms like local binary patterns and local directional
patterns than holistic algorithms like GLCM and Linear Discriminant
Analysis. The study proposes local binary pattern variant LBP-TOP,
local directional patterns and support vector machines aided by genetic
algorithms for feature selection. The study was based on Facial Expressions and Emotions (FEED) and CK+ image sources.
SELF-LEARNING AI FRAMEWORK FOR SKIN LESION IMAGE SEGMENTATION AND CLASSIFICATIONijcsit
Image segmentation and classification are the two main fundamental steps in pattern recognition. To perform medical image segmentation or classification with deep learning models, it requires training on large image dataset with annotation. The dermoscopy images (ISIC archive) considered for this work does not have ground truth information for lesion segmentation. Performing manual labelling on this dataset is time-consuming. To overcome this issue, self-learning annotation scheme was proposed in the two-stage deep learning algorithm. The two-stage deep learning algorithm consists of U-Net segmentation model with the annotation scheme and CNN classifier model. The annotation scheme uses a K-means clustering algorithm along with merging conditions to achieve initial labelling information for training the U-Net model. The classifier models namely ResNet-50 and LeNet-5 were trained and tested on the image dataset without segmentation for comparison and with the U-Net segmentation for implementing the proposed self-learning Artificial Intelligence (AI) framework. The classification results of the proposed AI framework achieved training accuracy of 93.8% and testing accuracy of 82.42% when compared with the two classifier models directly trained on the input images.
National Flags Recognition Based on Principal Component Analysisijtsrd
Recognizing an unknown flag in a scene is challenging due to the diversity of the data and to the complexity of the identification process. And flags are associated with geographical regions, countries and nations. But flag identification of different countries is a challenging and difficult task. Recognition of an unknown flag image in a scene is challenging due to the diversity of the data and to the complexity of the identification process. The aim of the study is to propose a feature extraction based recognition system for Myanmar's national flag. Image features are acquired from the region and state of flags which are identified by using principal component analysis PCA . PCA is a statistical approach used for reducing the number of features in National flags recognition system. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "National Flags Recognition Based on Principal Component Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26775.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26775/national-flags-recognition-based-on-principal-component-analysis/soe-moe-myint
IRJET- Prediction of Facial Attribute without Landmark InformationIRJET Journal
This document summarizes a research paper on predicting facial attributes without using landmark information. It describes the AFFAIR method which uses a global transformation network and part localization network to predict attributes. The global network learns an optimized transformation for each input face for attribute prediction. The part network localizes the most relevant facial regions for specific attributes. The global and local representations are then fused to predict attributes with high accuracy without requiring external landmark points. Experimental results on standard datasets show the AFFAIR method achieves state-of-the-art performance in landmark-free facial attribute prediction.
IRJET- Multiple Feature Fusion for Facial Expression Recognition in Video: Su...IRJET Journal
This document discusses multiple feature fusion techniques for facial expression recognition in videos. It reviews existing literature on using features like Histogram of Oriented Gradients from Three Orthogonal Planes (HOG-TOP), geometric warp features, Convolutional Neural Networks (CNNs), Scale Invariant Feature Transform (SIFT) and combining these features through multiple feature fusion to improve facial expression recognition from video sequences. The document also analyzes techniques like HOG-TOP for extracting dynamic textures from videos and geometric features to capture facial configuration changes caused by expressions.
This document summarizes a research paper that proposes a method for detecting and recognizing faces using the Viola Jones algorithm and Back Propagation Neural Network (BPNN).
The paper first discusses face detection and recognition challenges. It then provides background on Viola Jones algorithm and BPNN. The proposed methodology uses Viola Jones for face detection, converts the image to grayscale and binary, then trains segments or the whole image with BPNN. Results are analyzed using training, testing and validation curves in the MATLAB neural network tool to minimize error. In under 3 sentences, this document outlines the key techniques, proposed method, and analysis approach discussed in the source research paper.
Deep learning methods have improved machine capabilities for face recognition. Three key modules are typically used: 1) A face detector localizes faces in images using region-based or sliding window approaches with DCNNs. 2) A fiducial point detector localizes facial landmarks using DCNN regressors or 3D models. 3) A DCNN extracts features for face recognition and verification by computing similarity scores between representations. Large annotated datasets have enabled DCNNs to learn robust representations for unconstrained images.
Facial Expression Recognition Using SVM Classifierijeei-iaes
Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Computational facial expression analysis is a challenging research topic in computer vision. It is required by many applications such as human-computer interaction, computer graphic animation and automatic facial expression recognition. In recent years, plenty of computer vision techniques have been developed to track or recognize the facial activities in three levels. First, in the bottom level, facial feature tracking, which usually detects and tracks prominent landmarks surrounding facial components (i.e., mouth, eyebrow, etc), captures the detailed face shape information; Second, facial actions recognition, i.e., recognize facial action units (AUs) defined in FACS, try to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize facial expressions that represent the human emotion states. In this proposed algorithm initially detecting eye and mouth, features of eye and mouth are extracted using Gabor filter, (Local Binary Pattern) LBP and PCA is used to reduce the dimensions of the features. Finally SVM is used to classification of expression and facial action units.
IRJET- Brain Tumour Detection and ART Classification Technique in MR Brai...IRJET Journal
This document describes a proposed method for detecting and classifying brain tumors in MR brain images using robust principal component analysis (RPCA) and quad tree (QT) decomposition for image fusion. The method involves fusing T1 and T2 MRI images using RPCA and QT decomposition. The fused image is then segmented using level set segmentation. Features are extracted from the segmented image using complete local binary pattern (CLBP) and pyramid histogram of oriented gradients (PHOG) approaches. The features are then classified using an adaptive resonance theory (ART) classifier to classify the brain tumor as malignant or benign. The proposed method aims to efficiently fuse multi-modal MRI images for improved brain tumor detection and classification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document provides a review of different techniques for facial expression recognition. It begins with an introduction to facial expression recognition and its applications. It then discusses common preprocessing steps like noise removal and illumination correction. Next, it examines popular feature extraction methods like Gabor filters, Log-Gabor filters, and Local Binary Patterns. Dimensionality reduction techniques like PCA and LDA are also covered. Finally, common classification algorithms such as SVM and KNN that are used to categorize facial expressions are described. The document concludes that combining feature extraction methods like Log-Gabor filters with dimensionality reduction and classification techniques can help improve performance and accuracy of facial expression recognition systems.
Enhanced Face Detection Based on Haar-Like and MB-LBP FeaturesDr. Amarjeet Singh
The effective real-time face detection framework
proposed by Viola and Jones gained much popularity due its
computational efficiency and its simplicity. A notable
variant replaces the original Haar-like features with MBLBP (Multi-Block Local Binary Pattern) which are defined
by the local binary pattern operator, both detector types are
integrated into the OpenCV library. However, each
descriptor and its evaluation method has its own set of
strengths and setbacks. In this paper, an enhanced two-layer
face detector composed of both Haar-like and MB-LBP
features is presented. Haar-like features are employed as a
coarse filter but with a new evaluation involving dual
threshold. The already established MB-LBPs are arranged
as the fine filter of the detector. The Gentle AdaBoost
learning algorithm is deployed for the training of the
proposed detector to reach the classification and
performance potential. Experiments show that in the early
stages of classification, Haar features with dual threshold
are more discriminative than MB-LBP and original Haarlike features with respect to number of features required
and computation. Benchmarking the proposed detector
demonstrate overall 12% higher detection rate at 17% false
alarm over using MB-LBP features singly while performing
with ×3 speedup.
Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
The biometrics are used to identify a person effectively. In this paper, we propose optimised Face
recognition system based on log transformation and combination of face image features vectors. The face
images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation
is applied on enhanced image to generate features. The feature vectors of many images of a single person
image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is
used to compare test image feature vector with database feature vectors to identify a person. It is
experimented that, the performance of proposed algorithm is better compared to existing algorithms.
Facial expression recognition using pca and gabor with jaffe database 11748EditorIJAERD
This document discusses a facial expression recognition system that uses two different feature extraction methods - Principal Component Analysis (PCA) and Gabor filters - with the JAFFE facial expression database. PCA is used to reduce the dimensionality of the feature space, while Gabor filters are used to extract features due to their ability to encode spatial frequency and orientation information. The system that uses Gabor filters and PCA achieved better accuracy than one that used only PCA. The document provides mathematical background on PCA and Gabor filters and describes the steps of the facial expression recognition algorithm.
This document provides a comprehensive review of recent developments in content-based image retrieval and feature extraction. It discusses various low-level visual features used for image retrieval, including color, texture, shape, and spatial features. It also reviews approaches that fuse low-level features and use local features. Machine learning and deep learning techniques for content-based image retrieval are also summarized. The document concludes by discussing open challenges and directions for future research in this area.
Land Boundary Detection of an Island using improved Morphological OperationCSCJournals
Image analysis is one of the important tasks to obtain the information about earth surface. To detect and mark a particular land area, it is required to have the image from remote place. To recognize the same, the accurate boundary of that area has to be detected. In this paper, the example of remote sensing image has been considered. The accurate detection of the boundary is a complex task. A novel method has been proposed in this paper to detect the boundary of such land. Mathematical morphology is a simple and efficient method for this type of task. The morphological analysis is performed using structure elements (SE). By using mathematical morphology the images can be enhanced and then the boundary can be detected easily. Simultaneously the noise is removed by using the proposed model. The results exhibit the performance of the proposed method. Keywords: Remote Sensing images ; Edge detection; Gray- scale Morphological analysis, Structuring Element (SE).
This document describes a proposed system for identifying individual faces among a crowd using video footage. The system utilizes a training process involving face detection, feature extraction using HOG, and classification with SVM. Testing involves extracting video frames, detecting faces using LBP features, extracting HOG features, and classifying faces using SVM. The goal is to identify specific suspects from videos recorded with a CCTV camera mounted 2.5 meters high at a 60 degree angle, achieving the highest accuracy and lowest processing time from frame sampling and threshold testing.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
This document discusses facial expression recognition using local binary patterns and support vector machines. It begins by introducing facial expression recognition and its importance. It then describes preprocessing face images, detecting faces, extracting features using local binary patterns, and classifying expressions with support vector machines. Specifically, it details extracting LBP histograms from local regions of faces, concatenating them into a feature vector, and using an SVM for multi-class classification of expressions like happy, sad, angry, etc. Overall, the document provides an overview of the key steps involved in an automatic facial expression recognition system using LBP features and SVM classification.
Technique for recognizing faces using a hybrid of moments and a local binary...IJECEIAES
The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.
Implementation of features dynamic tracking filter to tracing pupilssipij
The objective of this paper is to show the implementation of an artificial vision filter capable of tracking the
pupils of a person in a video sequence. There are several algorithms that can achieve this objective, for this
case, features dynamic tracking selected, which is a method that traces patterns between each frame that
form a video scene, this type of processing offers the advantage of eliminating the problems of occlusion
patterns of interest. The implementation was tested on a base of videos of people with different physical
characteristics of the eyes. An additional goal is to obtain information of the eye movements that are
captured and pupil coordinates for each of these movements. These data could help some studies related to
eye health.
Learning in content based image retrieval a revieweSAT Journals
Abstract
Relevance feedback in Content Based Image Retrieval is an interactive process where the user provides feedback on the systemretrieved
images to bridge the gap between user semantics at high level and machine extracted low level features of images.
RF exploits Machine Learning and Pattern Recognition techniques for Short Term Learning and Long Term Learning to provide
improved performance in retrieval. Intra query and across query learning have received enormous attention over the past
decade. This paper first categorizes the various learning techniques and discusses the intuition behind each of these techniques.
State-of-art learning techniques ranging from Feature Relevance learning to manifold learning in STL and Latent Semantic
Analysis used in text processing to recent kernel semantic learning in LTL are discussed.
Keywords: Relevance Feedback, Short Term Learning, Long Term Learning, Sematic Gap, High Level Features
3D Dynamic Facial Sequences Analsysis for face recognition and emotion detectionTaleb ALASHKAR
This document outlines the PhD thesis of Taleb ALASHKAR on 3D dynamic facial sequence analysis for face recognition and emotion detection. The thesis proposes frameworks for 4D face recognition and 4D spontaneous emotion detection using subspace representations of 3D facial sequences and modeling trajectories on the Grassmann manifold. Experimental results on public databases show the frameworks achieve better recognition and detection performance than state-of-the-art methods, especially in expression-independent face recognition and early detection of emotions like happiness and pain.
Facial Expression Detection for video sequences using local feature extractio...sipij
Facial expression image analysis can either be in the form of
static image analysis or dynamic temporal 3D image or video analysis.
The former involves static images taken on an individual at a specific
point in time and is in 2-dimensional format. The latter involves dynamic textures extraction of video sequences extended in a temporal
domain. Dynamic texture analysis involves short term facial expression
movements in 3D in a temporal or spatial domain. Two feature extraction algorithms are used in 3D facial expression analysis namely holistic
and local algorithms. Holistic algorithms analyze the whole face whilst
the local algorithms analyze a facial image in small components namely
nose, mouth, cheek and forehead. The paper uses a popular local feature
extraction algorithm called LBP-TOP, dynamic image features based on
video sequences in a temporal domain. Volume Local Binary Patterns
combine texture, motion and appearance. VLBP and LBP-TOP outperformed other approaches by including local facial feature extraction
algorithms which are resistant to gray-scale modifications and computation. It is also crucial to note that these emotions being natural reactions, recognition of feature selection and edge detection from the video
sequences can increase accuracy and reduce the error rate. This can be
achieved by removing unimportant information from the facial images.
The results showed better percentage recognition rate by using local facial extraction algorithms like local binary patterns and local directional
patterns than holistic algorithms like GLCM and Linear Discriminant
Analysis. The study proposes local binary pattern variant LBP-TOP,
local directional patterns and support vector machines aided by genetic
algorithms for feature selection. The study was based on Facial Expressions and Emotions (FEED) and CK+ image sources.
SELF-LEARNING AI FRAMEWORK FOR SKIN LESION IMAGE SEGMENTATION AND CLASSIFICATIONijcsit
Image segmentation and classification are the two main fundamental steps in pattern recognition. To perform medical image segmentation or classification with deep learning models, it requires training on large image dataset with annotation. The dermoscopy images (ISIC archive) considered for this work does not have ground truth information for lesion segmentation. Performing manual labelling on this dataset is time-consuming. To overcome this issue, self-learning annotation scheme was proposed in the two-stage deep learning algorithm. The two-stage deep learning algorithm consists of U-Net segmentation model with the annotation scheme and CNN classifier model. The annotation scheme uses a K-means clustering algorithm along with merging conditions to achieve initial labelling information for training the U-Net model. The classifier models namely ResNet-50 and LeNet-5 were trained and tested on the image dataset without segmentation for comparison and with the U-Net segmentation for implementing the proposed self-learning Artificial Intelligence (AI) framework. The classification results of the proposed AI framework achieved training accuracy of 93.8% and testing accuracy of 82.42% when compared with the two classifier models directly trained on the input images.
National Flags Recognition Based on Principal Component Analysisijtsrd
Recognizing an unknown flag in a scene is challenging due to the diversity of the data and to the complexity of the identification process. And flags are associated with geographical regions, countries and nations. But flag identification of different countries is a challenging and difficult task. Recognition of an unknown flag image in a scene is challenging due to the diversity of the data and to the complexity of the identification process. The aim of the study is to propose a feature extraction based recognition system for Myanmar's national flag. Image features are acquired from the region and state of flags which are identified by using principal component analysis PCA . PCA is a statistical approach used for reducing the number of features in National flags recognition system. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "National Flags Recognition Based on Principal Component Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26775.pdfPaper URL: https://www.ijtsrd.com/other-scientific-research-area/other/26775/national-flags-recognition-based-on-principal-component-analysis/soe-moe-myint
IRJET- Prediction of Facial Attribute without Landmark InformationIRJET Journal
This document summarizes a research paper on predicting facial attributes without using landmark information. It describes the AFFAIR method which uses a global transformation network and part localization network to predict attributes. The global network learns an optimized transformation for each input face for attribute prediction. The part network localizes the most relevant facial regions for specific attributes. The global and local representations are then fused to predict attributes with high accuracy without requiring external landmark points. Experimental results on standard datasets show the AFFAIR method achieves state-of-the-art performance in landmark-free facial attribute prediction.
IRJET- Multiple Feature Fusion for Facial Expression Recognition in Video: Su...IRJET Journal
This document discusses multiple feature fusion techniques for facial expression recognition in videos. It reviews existing literature on using features like Histogram of Oriented Gradients from Three Orthogonal Planes (HOG-TOP), geometric warp features, Convolutional Neural Networks (CNNs), Scale Invariant Feature Transform (SIFT) and combining these features through multiple feature fusion to improve facial expression recognition from video sequences. The document also analyzes techniques like HOG-TOP for extracting dynamic textures from videos and geometric features to capture facial configuration changes caused by expressions.
This document summarizes a research paper that proposes a method for detecting and recognizing faces using the Viola Jones algorithm and Back Propagation Neural Network (BPNN).
The paper first discusses face detection and recognition challenges. It then provides background on Viola Jones algorithm and BPNN. The proposed methodology uses Viola Jones for face detection, converts the image to grayscale and binary, then trains segments or the whole image with BPNN. Results are analyzed using training, testing and validation curves in the MATLAB neural network tool to minimize error. In under 3 sentences, this document outlines the key techniques, proposed method, and analysis approach discussed in the source research paper.
Deep learning methods have improved machine capabilities for face recognition. Three key modules are typically used: 1) A face detector localizes faces in images using region-based or sliding window approaches with DCNNs. 2) A fiducial point detector localizes facial landmarks using DCNN regressors or 3D models. 3) A DCNN extracts features for face recognition and verification by computing similarity scores between representations. Large annotated datasets have enabled DCNNs to learn robust representations for unconstrained images.
Facial Expression Recognition Using SVM Classifierijeei-iaes
Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Computational facial expression analysis is a challenging research topic in computer vision. It is required by many applications such as human-computer interaction, computer graphic animation and automatic facial expression recognition. In recent years, plenty of computer vision techniques have been developed to track or recognize the facial activities in three levels. First, in the bottom level, facial feature tracking, which usually detects and tracks prominent landmarks surrounding facial components (i.e., mouth, eyebrow, etc), captures the detailed face shape information; Second, facial actions recognition, i.e., recognize facial action units (AUs) defined in FACS, try to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize some meaningful facial activities (i.e., lid tightener, eyebrow raiser, etc); In the top level, facial expression analysis attempts to recognize facial expressions that represent the human emotion states. In this proposed algorithm initially detecting eye and mouth, features of eye and mouth are extracted using Gabor filter, (Local Binary Pattern) LBP and PCA is used to reduce the dimensions of the features. Finally SVM is used to classification of expression and facial action units.
IRJET- Brain Tumour Detection and ART Classification Technique in MR Brai...IRJET Journal
This document describes a proposed method for detecting and classifying brain tumors in MR brain images using robust principal component analysis (RPCA) and quad tree (QT) decomposition for image fusion. The method involves fusing T1 and T2 MRI images using RPCA and QT decomposition. The fused image is then segmented using level set segmentation. Features are extracted from the segmented image using complete local binary pattern (CLBP) and pyramid histogram of oriented gradients (PHOG) approaches. The features are then classified using an adaptive resonance theory (ART) classifier to classify the brain tumor as malignant or benign. The proposed method aims to efficiently fuse multi-modal MRI images for improved brain tumor detection and classification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document provides a review of different techniques for facial expression recognition. It begins with an introduction to facial expression recognition and its applications. It then discusses common preprocessing steps like noise removal and illumination correction. Next, it examines popular feature extraction methods like Gabor filters, Log-Gabor filters, and Local Binary Patterns. Dimensionality reduction techniques like PCA and LDA are also covered. Finally, common classification algorithms such as SVM and KNN that are used to categorize facial expressions are described. The document concludes that combining feature extraction methods like Log-Gabor filters with dimensionality reduction and classification techniques can help improve performance and accuracy of facial expression recognition systems.
Enhanced Face Detection Based on Haar-Like and MB-LBP FeaturesDr. Amarjeet Singh
The effective real-time face detection framework
proposed by Viola and Jones gained much popularity due its
computational efficiency and its simplicity. A notable
variant replaces the original Haar-like features with MBLBP (Multi-Block Local Binary Pattern) which are defined
by the local binary pattern operator, both detector types are
integrated into the OpenCV library. However, each
descriptor and its evaluation method has its own set of
strengths and setbacks. In this paper, an enhanced two-layer
face detector composed of both Haar-like and MB-LBP
features is presented. Haar-like features are employed as a
coarse filter but with a new evaluation involving dual
threshold. The already established MB-LBPs are arranged
as the fine filter of the detector. The Gentle AdaBoost
learning algorithm is deployed for the training of the
proposed detector to reach the classification and
performance potential. Experiments show that in the early
stages of classification, Haar features with dual threshold
are more discriminative than MB-LBP and original Haarlike features with respect to number of features required
and computation. Benchmarking the proposed detector
demonstrate overall 12% higher detection rate at 17% false
alarm over using MB-LBP features singly while performing
with ×3 speedup.
Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
The biometrics are used to identify a person effectively. In this paper, we propose optimised Face
recognition system based on log transformation and combination of face image features vectors. The face
images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation
is applied on enhanced image to generate features. The feature vectors of many images of a single person
image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is
used to compare test image feature vector with database feature vectors to identify a person. It is
experimented that, the performance of proposed algorithm is better compared to existing algorithms.
Facial expression recognition using pca and gabor with jaffe database 11748EditorIJAERD
This document discusses a facial expression recognition system that uses two different feature extraction methods - Principal Component Analysis (PCA) and Gabor filters - with the JAFFE facial expression database. PCA is used to reduce the dimensionality of the feature space, while Gabor filters are used to extract features due to their ability to encode spatial frequency and orientation information. The system that uses Gabor filters and PCA achieved better accuracy than one that used only PCA. The document provides mathematical background on PCA and Gabor filters and describes the steps of the facial expression recognition algorithm.
This document provides a comprehensive review of recent developments in content-based image retrieval and feature extraction. It discusses various low-level visual features used for image retrieval, including color, texture, shape, and spatial features. It also reviews approaches that fuse low-level features and use local features. Machine learning and deep learning techniques for content-based image retrieval are also summarized. The document concludes by discussing open challenges and directions for future research in this area.
Land Boundary Detection of an Island using improved Morphological OperationCSCJournals
Image analysis is one of the important tasks to obtain the information about earth surface. To detect and mark a particular land area, it is required to have the image from remote place. To recognize the same, the accurate boundary of that area has to be detected. In this paper, the example of remote sensing image has been considered. The accurate detection of the boundary is a complex task. A novel method has been proposed in this paper to detect the boundary of such land. Mathematical morphology is a simple and efficient method for this type of task. The morphological analysis is performed using structure elements (SE). By using mathematical morphology the images can be enhanced and then the boundary can be detected easily. Simultaneously the noise is removed by using the proposed model. The results exhibit the performance of the proposed method. Keywords: Remote Sensing images ; Edge detection; Gray- scale Morphological analysis, Structuring Element (SE).
This document describes a proposed system for identifying individual faces among a crowd using video footage. The system utilizes a training process involving face detection, feature extraction using HOG, and classification with SVM. Testing involves extracting video frames, detecting faces using LBP features, extracting HOG features, and classifying faces using SVM. The goal is to identify specific suspects from videos recorded with a CCTV camera mounted 2.5 meters high at a 60 degree angle, achieving the highest accuracy and lowest processing time from frame sampling and threshold testing.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
This document discusses facial expression recognition using local binary patterns and support vector machines. It begins by introducing facial expression recognition and its importance. It then describes preprocessing face images, detecting faces, extracting features using local binary patterns, and classifying expressions with support vector machines. Specifically, it details extracting LBP histograms from local regions of faces, concatenating them into a feature vector, and using an SVM for multi-class classification of expressions like happy, sad, angry, etc. Overall, the document provides an overview of the key steps involved in an automatic facial expression recognition system using LBP features and SVM classification.
Technique for recognizing faces using a hybrid of moments and a local binary...IJECEIAES
The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.
Face Recognition Smart Attendance System- A SurveyIRJET Journal
This document surveys 15 research papers on face recognition smart attendance systems. It summarizes each paper's methodology, including the databases and images used, feature extraction and matching algorithms like PCA, LDA, CNN, techniques for addressing issues like lighting and pose variations, and the accuracy and limitations of each system. Overall, the papers presented a variety of approaches to developing face recognition systems for automated student attendance, comparing methods like PCA, LDA, HOG, and deep learning algorithms and evaluating factors like recognition rate, robustness, and speed.
Face Recognition Smart Attendance System: (InClass System)IRJET Journal
- The document describes a face recognition system called "InClass" to automate student attendance tracking. It aims to address issues with traditional manual attendance systems like being inaccurate, time-consuming, and difficult to maintain.
- The InClass system uses a CNN face detector to detect and identify students' faces from images captured with a camera. It can handle variations in lighting, angles, and occlusions. Matching faces to a database allows for automated attendance marking.
- The system aims to simplify the attendance process, reduce time and errors compared to existing biometric systems, and make attendance records easily accessible and storable digitally rather than on paper.
Virtual Contact Discovery using Facial RecognitionIRJET Journal
The document describes a project that aims to use facial recognition as a means of contact discovery and metadata retrieval. The project seeks to optimize machine learning models for facial detection and verification in order to provide fast and accurate contact matching based on facial encodings. It outlines the objectives, scope, literature review, proposed system architecture and implementation details. The system would take facial landmarks and encodings to compare and rank the top 10 most similar encodings to identify matches from a database. The optimized model aims to reduce latency and improve accuracy for contact matching based on facial scans.
Progression in Large Age-Gap Face VerificationIRJET Journal
1. The document discusses progression in large age-gap face verification. It summarizes various techniques used for face recognition from conventional methods to deep neural networks.
2. A typical face recognition system includes image acquisition, face detection, normalization, feature extraction, and then matching. The document reviews several feature extraction methods like Eigenfaces, Fisherfaces, and local binary patterns.
3. Recent deep learning approaches like convolutional neural networks have improved face recognition accuracy. One study used CNNs with metric learning and achieved high accuracy on the Labeled Faces in the Wild dataset. Encoding image microstructures and identity factor analysis has also been effective for matching similar faces.
Survey of The Problem of Object Detection In Real ImagesCSCJournals
Object detection and recognition are important problems in computer vision. Since these problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real-time, and dynamic object detection/recognition methods are still unavailable. The accuracy level of any algorithm or even Google glass project is below 16% for over 22,000 object categories. With this accuracy, it’s practically unusable. This paper reviews the various aspects of object detection and the challenges involved. The aspects addressed are feature types, learning model, object templates, matching schemes, and boosting methods. Most current research works are highlighted and discussed. Decision making tips are included with extensive discussion of the merits and demerits of each scheme.
IRJET - Facial In-Painting using Deep Learning in Machine LearningIRJET Journal
This document describes a deep learning approach for facial in-painting to generate plausible facial structures for missing pixels in face images. The approach uses the observable region of a face as well as inferred attributes like gender, ethnicity, and expression. It selects a guidance face matching the attributes to in-paint missing areas like the mouth or eyes. In-painting is done on intrinsic image layers instead of RGB to handle illumination differences. The proposed method uses face detection, alignment, representation, and matching with real-time datasets to fill missing or masked regions with realistic synthesized content.
Real time multi face detection using deep learningReallykul Kuul
This document proposes a framework for real-time multiple face recognition using deep learning on an embedded GPU system. The framework includes face detection using a CNN, face tracking to reduce processing time, and a state-of-the-art deep CNN for face recognition. Experimental results showed the system can recognize up to 8 faces simultaneously in real-time, with processing times up to 0.23 seconds and a minimum recognition rate of 83.67%.
Face Images Database Indexing for Person Identification ProblemCSCJournals
Face biometric data are with high dimensional features and hence, traditional searching techniques are not applicable to retrieve them. As a consequence, it is an issue to identify a person with face data from a large pool of face database in real-time. This paper addresses this issue and proposes an indexing technique to narrow down the search space. We create a two level index space based on the SURF key points and divide the index space into a number of cells. We define a set of hash functions to store the SURF descriptors of a face image into the cell. The SURF descriptors within an index cell are stored into kd-tree. A candidate set is retrieved from the index space by applying the same hash functions on the query key points and kd-tree based nearest neighbor searching. Finally, we rank the retrieved candidates according to their occurrences. We have done our experiment with three popular face databases namely, FERET, FRGC and CalTech face databases and achieved 95.57%, 97.00% and 92.31% hit rate with 7.90%, 12.55% and 23.72% penetration rate for FERET, FRGC and CalTech databases, respectively. The hit rate increases to 97.78%, 99.36% and 100% for FERET, FRGC and CalTech databases, respectively when we consider top fifty ranks. Further, in our proposed approach, it is possible to retrieve a set of face templates similar with query template in the order of milliseconds. From the experimental results we can substantiate that application of indexing using hash function on SURF key points is effective for fast and accurate face image retrieval.
FaceDetectionforColorImageBasedonMATLAB.pdfAnita Pal
The document proposes a method for face detection in color images using MATLAB and a convolutional neural network (CNN) with two layers. It begins with an introduction to face detection technologies and challenges. It then describes the proposed methodology, which involves reading a color image, applying CNN convolutions to detect faces, and fusing the results. Mathematical aspects of CNNs are discussed, including equations for convolutional operations and dimensions. The CNN method combines feature extraction, segmentation, and classification. Finally, the document summarizes how the proposed algorithm was designed in MATLAB to identify faces using a simple learning algorithm and CNNs with fewer layers than traditional networks. The goal is to develop a fast face detection program for color images based on new deep learning standards.
FACE PHOTO-SKETCH RECOGNITION USING DEEP LEARNING TECHNIQUES - A REVIEWIRJET Journal
This document reviews various deep learning techniques for face photo-sketch recognition. It summarizes several studies that used deep learning and other machine learning methods to match faces across photo and sketch domains. Feature injection and convolutional neural networks, knowledge distillation frameworks, relational graph modules, domain alignment embedding networks, and using 3D morphable models and CNNs for forensic sketch recognition are some of the techniques discussed. The studies aim to improve cross-domain face recognition accuracy, which remains challenging due to differences in photo and sketch representations. Accuracies ranging from 73-95% are reported for the various methods.
Multi Local Feature Selection Using Genetic Algorithm For Face IdentificationCSCJournals
This document presents a face recognition algorithm that uses a multi-local feature selection approach based on genetic algorithms and pseudo Zernike moment invariants. The algorithm involves five stages: 1) face detection using an ellipse to approximate the face region, 2) extraction of facial features (eyes, nose, mouth) within regions using genetic algorithms to locate templates with maximum edge density, 3) generation of moment invariants from the facial features using pseudo Zernike polynomials, 4) classification of facial features using radial basis function neural networks, and 5) selection of multiple local features for face identification. The algorithm was tested on over 3000 images from three databases, achieving recognition rates over 89% which is higher than global or single local feature approaches and
INVESTIGATING THE EFFECT OF BD-CRAFT TO TEXT DETECTION ALGORITHMSijaia
With the rise and development of deep learning, computer vision and document analysis has influenced the
area of text detection. Despite significant efforts in improving text detection performance, it remains to be
challenging, as evident by the series of the Robust Reading Competitions. This study investigates the impact
of employing BD-CRAFT – a variant of CRAFT that involves automatic image classification utilizing a
Laplacian operator and further preprocess the classified blurry images using blind deconvolution to the
top-ranked algorithms, SenseTime and TextFuseNet. Results revealed that the proposed method
significantly enhanced the detection performances of the said algorithms. TextFuseNet + BD-CRAFT
achieved an outstanding h-mean result of 93.55% and shows an impressive improvement of over 4%
increase to its precision yielding 95.71% while SenseTime + BD-CRAFT placed first with a very
remarkable 95.22% h-mean and exhibited a huge precision improvement of over 4%.
Investigating the Effect of BD-CRAFT to Text Detection Algorithmsgerogepatton
The document summarizes a study that investigated the effect of applying a blind deconvolution technique called BD-CRAFT to improve the performance of two state-of-the-art text detection algorithms, SenseTime and TextFuseNet. BD-CRAFT automatically classifies images as blurry or non-blurry using a Laplacian operator threshold, and applies blind deconvolution to deblur the blurry images. The study found that combining BD-CRAFT with SenseTime and TextFuseNet significantly improved their text detection performances on the ICDAR 2013 dataset, with TextFuseNet + BD-CRAFT achieving a 93.55% h-mean and SenseTime + BD-CRAFT
Facial Expression Recognition System Based on SVM and HOG TechniquesCSCJournals
Facial expression is one of the most commonly used nonverbal means by humans to transmit internal emotional states and, therefore, it plays a fundamental role in interpersonal interactions. Although there is a wide range of possible facial expressions, psychologists have identified six fundamental ones (happiness, sadness, surprise, anger, fear and disgust) that are universally recognized. Automatic facial expression recognition (FER) is a topic of growing interest mainly due to the rapid spread of assistive technology applications, as human–robot interaction, where a robust emotional awareness is a key point to best accomplish the assistive task. The proposed work aims to design a robust facial expression recognition system (FER). FER system can be divided into three modules, namely facial registration, feature extraction and classification. The objective of this work is the recognition of facial expressions based the Histogram of Oriented Gradients (HOG) and support vector machine (SVM) algorithm.
Facial image retrieval on semantic features using adaptive mean genetic algor...TELKOMNIKA JOURNAL
The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.
IRJET - Factors Affecting Deployment of Deep Learning based Face Recognition ...IRJET Journal
This document discusses factors affecting the deployment of deep learning models for face recognition on smartphones. It examines training data requirements, suitable neural network architectures, and effective loss functions. Larger datasets with more subjects and images are preferred for training models that generalize well. Residual networks like ResNet have achieved good accuracy while being efficient for face recognition. Loss functions like center loss and triplet loss help learn discriminative features by reducing intra-class and increasing inter-class variations.
AN EFFICIENT FACE RECOGNITION EMPLOYING SVM AND BU-LDPIRJET Journal
The document presents a study on an efficient face recognition method employing support vector machines (SVM) and biomimetic uncorrelated local difference projection (BU-LDP). The study proposes using BU-LDP, which is based on uncorrelated local projection but uses a different neighborhood coefficient calculation approach inspired by human perception. Experimental results on several datasets show that BU-LDP and its kernel variant KBU-LDP outperform state-of-the-art methods for face recognition. Future work will focus on addressing the "one sample problem" and applying the approach to unlabeled data.
IRJET- A Review on Various Techniques for Face DetectionIRJET Journal
This document provides a review of various techniques for face detection. It summarizes several approaches including Viola Jones, genetic algorithms, convolutional neural networks, support vector machines, Hough transforms with convolutional neural networks, MMX feature extraction, and Minmax with embedding. For each approach, it discusses the methodology, advantages, and disadvantages. It finds that while each approach has benefits for face detection, they also have limitations and no single approach is clearly superior in all cases and environments. The goal of the study is to systematically compare the different techniques to better understand how each contributes to success in face detection.
Similar to Feature Fusion and Classifier Ensemble Technique for Robust Face Recognition (20)
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Feature Fusion and Classifier Ensemble Technique for Robust Face Recognition
1. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 1
Feature Fusion and Classifier Ensemble Technique for Robust
Face Recognition
Hamayun A. Khan h.khan@arabou.edu.kw
Faculty of Computer Studies/ Arab Open
University
Industrial Ardiya, 13033, Kuwait
Abstract
Face recognition is an important part of the broader biometric security systems research. In the
past, researchers have explored either the Feature Space or the Classifier Space at a time to
achieve efficient face recognition. In this work, both the Feature Space optimization as well as the
Classifier Space optimization have been used to achieve improved results. The efficient
technique of Feature Fusion in the Feature Space and Classifier Ensemble technique in the
Classifier Space have been used to achieve robust and efficient face recognition. In the Feature
Space, the Discrete Wavelet Transform (DWT) and the Histogram of Oriented Gradient (HOG)
features have been extracted from face images and these have been used for classification
purposes after Feature Fusion using the Principal Component Analysis (PCA). In the Classifier
Space, a Classifier Ensemble has been used, utilizing the bagging technique for ensemble
training, instead of a single classifier for efficient classification. Proper selections of various
parameters of the DWT, HOG features and the Classification Ensemble have been considered to
achieve optimum performance. The proposed classification technique has been applied to the
AT&T (ORL) and Yale benchmark face recognition databases, and we have achieved excellent
results of 99.78% and 97.72% classification accuracy respectively. The proposed Feature Fusion
and Classifier Ensemble technique has been subjected to sensitivity analysis and it has been
found to be robust under reduced spatial resolution conditions.
Keywords: Face Recognition, Feature Space, Wavelet Analysis, HOG Descriptors, Principal
Component Analysis, Feature Fusion, Classifier Space, Classifier Ensemble, Linear Discriminant
Classifiers, Robust Classification, Cross Validation.
1. INTRODUCTION
Biometric security systems are an important area of research and face recognition is a key
element of the biometric identification systems [1, 2]. The recent proliferation of image capturing
technologies and abundant availability of face images has given impetus to finding more efficient
solutions to the face recognition problem. Face recognition is an essential part of the law
enforcement work since Closed Caption TV (CCTV) systems and image capturing technologies
are widely used for law enforcement purposes. Security systems can utilize efficient face
recognition algorithms in order to identify any person wanted by law. Access control systems can
use face recognition along with other access control technologies to enable access to premises
or resources. Apart from law enforcement purposes, face recognition can be used in social
networks, commercial and general web search applications. The availability of large amount of
face images on the web makes face recognition important for web search applications. Popular
social networking sites such as Instagram and Pinterest are heavily image focused. Searching
large image databases on social networks can make use of face recognition techniques to
perform broader and effective searches. The use of face recognition technology has to be
balanced against the intended purpose and the various issues related to privacy and
confidentiality. The need to apply face recognition technology in order to identify and catch, say, a
purse snatching thief is understandable on one hand but to apply large scale face recognition
2. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 2
systems to tag photos on the other hand may involve privacy issues which need to be addressed
carefully [1, 2].
Past research in the area of Face Recognition has focused either to exploit the Feature Space or
the Classifier Space at a time for face recognition. In this work a new methodology for face
recognition has been proposed to optimize processes both in the Feature Space as well as in the
Classifier Space for achieving efficient face recognition. In the Feature Space, it is proposed to
extract 3 sets of features from face images and then to use Feature Fusion to fuse them together
through PCA technique to get a compact feature vector. In the Classifier Space it is proposed to
use a Classifier Ensemble instead of a single classifier for classification purposes. By optimizing
the processes both in the Feature Space as well as in the Classifier Space, it will be shown that
superior classification performance is achievable. Also, it will be shown that the proposed
technique is robust under degradation of spatial resolution conditions since the classification
performance of the proposed system is slightly degraded when the spatial resolution is reduced
by half.
The breakdown of this article is given below. A review of previous work on face recognition is
presented in Section 2 of this paper. In Section 3, the proposed robust system for face
recognition has been introduced. The Feature Space and the Classifier Space optimizations have
been discussed in detail in Section 4. The overall setup of the proposed system has been
presented in Section 5. Results and discussions have been presented in Section 6. Comparison
of results with existing techniques has been discussed in Section 7. Section 8 presents the
conclusions and future work ideas.
2. RELATED WORK
The problem of face recognition has been studied extensively for a number of years. Researchers
have applied a wide variety of techniques to explore the face recognition problem. Excellent
surveys on face recognition systems and techniques are available in [3, 4]. Chihaoui et al. [3]
have provided a comprehensive survey of face recognition systems till the year 2016, whereas
Jafri and Arabnia [4] have covered the research period till the year 2009. As discussed in great
detail in [3], the 2-Dimensional (2D) face recognition techniques can be broadly divided into 4
categories i.e. Global (or Holistic) Approaches, Local Approaches, Hybrid Approaches, also
including Statistical Models, and Modern Techniques. In the first category of the Global Methods,
the whole face image is considered globally without the need to extract regions of interests such
as eyes, mouth, nose, etc. The techniques in this category are further subdivided into Linear and
Non-Linear methods. In the Linear techniques, the images are mapped into a smaller sub-space
using linear projections [3]. The Linear techniques include the classical Eigenface Methodology
[5--7], 2D Principal Component Analysis (PCA) [8], Independent Component Analysis (ICA) [9],
Linear Discriminant Analysis (LDA) [10], etc. In the Non-Linear methodology, a ‘kernel’ function is
used to transform the problem into another larger space in which the problem becomes linear.
The Non-Linear techniques include the Kernel PCA (KPCA) [11], Kernel Direct Discriminant
Analysis [12], Kernel ICA (KICA) [13], ISOMAPs [14], etc.
In the second category of Local Approaches, some particular facial features are used for
recognition purposes [3]. The various techniques in this category include the Dynamic Link
Architecture (DLA) based on the deformable topological graph [15], Elastic Bunch Grapg
Matching (EBGM) [16], Geometric Feature Vector [17], Gabor Filters [18], Scale-Invariant Feature
Transform (SIFT) [19], Local Binary Patterns(LBP) [20], etc.
In the third category of Hybrid Approaches, a combination of the Global and Local techniques is
used. This category also makes use of Statistical Models i.e. a statistical model representing the
face is constructed and used for recognition purposes [3]. The various techniques in this category
include Hidden Markov Models (HMMs) [21], the Gabor Wavelet Transform based on the Psuedo
HMM (GWT-PHMM) [22], Discrete Cosine Transform HMM (DCT-HMM) [23], HMM-LBP [24], etc.
The fourth category of Modern Techniques [3] include 3D face recognition [25--27], Multimodal
3. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 3
systems [28], Infrared systems [29--31] and Deep Learning Sytems [32--36]. The Deep Learning
Systems are currently quite popular for Machine Learning and face recognition tasks and they
generally use Convolutional Neural Networks (CNNs) for image recognition requiring large
datasets for learning purposes. Big Corporations such as Google and Facebook have their own
Deep Learning based systems with high accuracies including Deepface and Facenet respectively
[37, 38]. Opensource software libraries, such as OpenFace, are also available for face
recognition purposes [39].
In this work, a new methodology for face recognition has been proposed which differentiates itself
from current technologies in that it optimizes both the Feature Space as well as the Classifier
Space instead of focusing on one at a time. An initial version of this work using only the DWT
based feature set was presented by the authors in [40]. A new feature set based on the HOG
descriptors has been used in the current work along with DWT features and this has enabled us
to achieve improved classification accuracy. The current work has also subjected the system to
detailed sensitivity analysis using lower spatial resolution images. Three different feature sets are
used in the Feature Space and these are fused together to achieve an optimized feature set that
maximizes classification accuracy. In the Classifier Space, the Classifier Ensemble technique is
used instead of a single classifier to achieve improved classification results. These optimizations
in both the spaces have enabled us to develop a robust and efficient face recognition system.
3. THE PROPOSED ROBUST FACE RECOGNITION SYSTEM
In this work, a robust face recognition system is proposed based on optimizing the various
processes both in the Feature Space as well as in the Classifier Space. The system architecture
of the proposed face recognition system is shown in Figure 1 below.
FIGURE 1: Proposed Robust Face Recognition System.
The proposed system mainly consists of the Feature Space processing and then the subsequent
Classifier Space processing. Face Detection is also an essential part of face recognition systems
but since the benchmark face databases that are considered in this work include mainly frontal
images therefore the Face Detection part has not been used. The Feature Space processing
includes the Pre-processing step, the Feature Extraction step and the subsequent Feature Fusion
step. For Feature Extraction the 2-D DWT decomposition and the HOG descriptors have been
used. Feature Fusion is based on the PCA and it is used to fuse the extracted features into a
compact feature vector.
The Classifier Space processing consists of the Classifier Ensemble which is constructed from a
number of individual base classifiers. In this work the Classification Ensemble class of Matlab
with Linear Discriminant Analysis (LDA) classifiers as base classifiers have been used to
construct the ensemble system. A set of 30 base classifiers constitute the ensemble. The
Bagging Technique has been used to train the ensemble for face recognition task and the 10-fold
cross validation technique has been employed to validate the classifier system. 50 runs of our
simulations are performed and then the average of these 50 runs is taken as the final
performance of our system. The classification performance is presented in the form of
classification accuracy. The proposed system is based on the concept of optimising the various
Faces
Database
Image
Pre-
Processing
Feature
Extraction
Classifier 1
Classifier N
Vote
4. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 4
processes both in the Feature Space as well as in the Classifier Space. Details about the
proposed recognition system are given in the following section.
4. FEATURE SPACE AND CLASSIFIER SPACE OPTIMIZATION
4.1 Benchmark Face Databases
Two benchmark face image databases i.e. the AT&T Database of faces [41], formerly known as
the Olevetti Research Lab (ORL) Database of faces, and the Yale face image database [42] have
been used for experimental purposes. Images of two spatial resolutions i.e. size 64X64 pixels and
32X32 pixels for both databases have been considered in this work.
The AT&T (ORL) database of face images is maintained by AT&T laboratories, Cambridge and it
consists of images of 40 subjects each having 10 face images in the set for a total of 400 images
[41]. The face images in the database are mostly frontal images with moderate facial
expressions. The database does not suffer from any major Pose, Illumination and Expression
(PIE) problem but it has slight Illumination and Facial Expression variations. The original face
images in this database are of size 92x112 but images of two reduced spatial resolutions i.e.
64X64 pixels and 32X32 pixels have been considered in this work. A snapshot of images from the
AT&T (ORL) Database is shown in Figure 2. The main reason for selecting smaller sizes for
images is to evaluate the performance of the system on reduced spatial resolution images.
FIGURE 2: A snapshot of the images from AT&T (ORL) Database.
The second benchmark face database considered in this work is the Yale Database [42]. This
database consists of face images of 15 subjects with 11 images each for a total of 165 images.
This database is more challenging than the AT&T (ORL) Database since it exhibits comparatively
more Illumination and Expression variations. The original face images in this database are of
sizes 320x243 and 100x80 but here too images of 64X64 and 32X32 spatial resolution have been
considered in this work for the same reason as mentioned above for the AT&T (ORL) Database.
The various facial expressions exhibited by the subjects in this database include several
expressions, such as smiling faces, yawning, winking and frowning. A snapshot of images from
the Yale Database is shown in Figure 3.
5. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 5
FIGURE 3: A snapshot of the images from Yale Database.
4.2 Image Pre-Processing Step
The image pre-processing step is usually required for the images that are challenging to classify.
Images that exhibit the Pose, Illumination and Expression (PIE) problem fall in the challenging
category of images for classification tasks. The partial PIE problem is exhibited by the Yale
database compared to the AT&T (ORL) database. In this work, the adaptive histogram
equalization technique has been used in order to alleviate the illumination problem from images
and make them more suitable for classification. An improvement in classification accuracy has
been achieved when the pre-processing is applied to the Yale database. Preprocessing is not
usually required for the AT&T (ORL) database since the images are mostly frontal with little PIE
problems.
4.3 HOG Based Features Extraction
The Histogram of Oriented Gradient (HOG) is an efficient technique that has been applied for
human detection [43]. The performance of HOG based classification depends on the proper
selection of cell size used for processing the face images to extract the HOG descriptors. This
technique involves proper selection of the 2 element vector for the cell sizes. Figure 4 below
depicts this phenomenon. We have observed that selecting a very small cell size, say 2x2 pixels,
compared to face image of size 64X64, results in a large size HOG feature vector that tends to
over burden the classifiers resulting in lower classification accuracy. On the other hand, selecting
a very large cell size, say 10x10 pixels, results in a feature vector of smaller size which again is
inappropriate for classification purposes. It has been experienced in this work that cell sizes of
7x7 pixels and 5X5 pixels for face images of size 64X64 and 32X32 respectively result in
optimum classification performance.
FIGURE 4: Effect of Cell Size for HOG Feature Extraction.
6. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 6
Hence for the HOG based feature set, we have used cell sizes of 7x7 pixels and 5x5 pixels for
the 64x64 and 32x32 spatial resolution face images respectively. The resulting feature vector
based on HOG descriptors is denoted by 'Features_HOG'. As an example, for a 64X64 size
image, we use a size 7x7 pixels cell element to compute the HOG features. This results in a
2304x1 size HOG feature vector.
4.4 DWT Based Feature Extraction
The Discrete Wavelet Transform (DWT) is a well-established technique for image analysis [44,
45]. It enables multi-resolution analysis of images at different scales and resolutions. Thus it is
possible to extract rich wavelet based features from images which can be used for classification
purposes. The DWT can be expressed by the following set of equations [45]:
(1)
(2)
i = {H, V, D}
The functions (x,y) and ψ (x,y) are the scaling function and wavelets respectively. We perform
2 levels of decomposition of the face images and the computed coefficients form the DWT feature
vector for each image. The DWT decomposition results in both the Detailed and the
Approximation Coefficients. We use the Approximation Coefficients as feature vectors since they
carry useful information about the images. A Level 2 DWT decomposition of one of the face
images from the AT&T (ORL) database is shown in Figure 5 below. Hence we have used Level 1
and 2 DWT decompositions in our work to extract optimum DWT features, denoted as
'Approx_Coeffs_DWT_Level1' and 'Approx_Coeffs_DWT_Level2', for improved classification
accuracy.
FIGURE 5: Wavelet Analysis of a Sample Face Image.
For the individual Wavelet Function, the Daubachies Wavelet ‘db1’ has been used. For instance,
for a size 64X64 image, we compute the approximation coefficients from the image at 2
decomposition levels i.e Level1 and Level2. We use the Approximation Coefficients as feature
7. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 7
vectors based on DWT coefficients resulting in a 1024X1 DWT feature vector at Level1 and
256x1 at Level2. Thus the DWT based feature vector consists of the following:
Features_DWT=[Approx_Coeffs_DWT_Level1 Approx_Coeffs_DWT_Level2] (3)
4.5 Feature Fusion for Robust Face Recognition
The individual feature vectors computed using the HOG and DWT analysis i.e. Features_HOG
and Features_DWT are normalized before concatenating them to form the overall feature vector.
The normalization is essential so as to ensure that individual feature vector values do not
overshadow other values. After normalization, the DWT and HOG features of each image are put
together to form an overall feature vector.
Features_Overall = [Features_HOG Features_DWT] (4)
The 2 level decompositions of the images by the DWT analysis lead to high dimensional feature
vectors. Also, the HOG analysis results in high dimensional feature vector. Using high
dimensional feature vectors for classification purposes often leads to poor performance of
classifiers. Hence it is desirable to reduce the dimensionality of feature vectors to reduce
computational cost as well as to achieve better performance. We utilize the PCA technique for
dimensionality reduction as well as the fusion of the individual feature vectors to achieve the final
feature vector, denoted as 'Features_Fused', which is used for classification purposes.
Features_Fused = PCA{ Features_Overall } (5)
The fusion of feature values results in better performance of the ensemble classifier since it
utilizes the combined information contained in the DWT and the HOG analysis. Choosing an
appropriate dimension for Feature Fusion has been based on the resulting best performance of
the classification system. For instance, for a 64X64 size image AT&T(ORL) image, we have used
a PCA Dimensionality of 50 for Feature fusion since it maximizes the classification accuracy.
The sample feature vector values for the AT&T (ORL) and Yale databases obtained after fusion,
using dimensionality of 50, are shown in Figure 6 below. The figure shows plots of values for
three different classes for each database. It is clear from the figure that same class values show
strong similarity trends whereas the intra-class differences are also evident thus highlighting the
distinguishing characteristics of the features.
FIGURE 6: Sample Fused Feature values (Dim=50) for the 2 Databases for 3 classes.
4.6 Classifier Space Optimization Through LDA Based Ensemble
At the classification stage, an Ensemble of Classifiers has been used instead of a single
classifier. The Ensemble or team of classifiers approach is superior to using single classifier since
8. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 8
it uses the combined or collective classification capabilities of the ensemble to achieve superior
classification performance. As mentioned above, the ensemble system used in our work uses 30
Linear Discriminant Analysis (LDA) based classifiers as base classifiers. The base classifiers are
usually weak classifiers but by acting together in the ensemble system as a team the overall
performance of the ensemble becomes superior to the base classifiers. The choice of LDA base
classifiers is important since these maximize the between-class distances and minimize the in-
class separations thus leading to higher classification accuracies.
The ensemble systems are commonly trained using the Boosting or the Bagging methodologies
for ensemble learning [46]. In this work the ensemble has been trained on the face image
features data obtained after feature fusion using the Bagging Technique for ensemble learning.
Bagging or “bootstrap aggregation” uses resampled version of features data to train the
ensemble. The cross validation methodology has been used to assess the performance of the
ensemble system on the 2 benchmark databases. This methodology is useful in situations where
the data set is not large enough to partition it into an independent Training Set and Test Set.
Specifically, we have used the 10-fold cross validation methodology in our work to judge the
classification accuracy of our system.
5. OVERALL SYSTEM SETUP
The overall setup of the proposed face recognition system depends on the architecture of the
system as shown in Figure 2. The setup information of our system is related to the sub-blocks of
our system and this information is presented in Table 1 below. For the Face Database sub-block,
we have considered the Yale and AT&T (ORL) benchmark databases in our work. We have
applied preprocessing operation only to the Yale database images as they suffer from the PIE
problem. For feature extraction purposes, we have used the DWT and the HOG analysis based
techniques. We have used 2 decomposition levels for our DWT based feature set and we have
used the Wavelet db1 in our computations. For the fusion sub-block, the PCA technique has been
used for dimensionality reduction and to fuse together the individual feature vectors to achieve an
efficient feature vector that packs the discriminating powers of the individual features. For the
classification part, we have used an ensemble of 30 Linear Discriminant Analysis (LDA) based
classifiers as base classifiers in our classifier ensemble.
Item/Factor AT&T (ORL)
Database
Yale Database
Total subjects in Database 40 15
Number of images of each subject 10 11
Total Number of Images 400 165
Image resolutions Size 64X64 & 32X32 Size 64X64 & 32X32
Preprocessing of images
None Adaptive Histogram
Equalization
Feature extraction methodology
2-D DWT and HOG
Features
2-D DWT and HOG
Features
Wavelets used
Daubechies
"db1"
Daubechies
"db1"
Wavelet decomposition levels Level 1 & 2 Level 1 & 2
HOG cell size for 64x64 images 7x7 pixels 7x7 pixels
HOG cell size for 32x32 images 5x5 pixels 5x5 pixels
Feature Fusion Technique and Dimensionality
reduction
PCA PCA
Type of Base Classifiers Linear Discriminant Linear Discriminant
Number of base classifiers 30 30
Testing/Validation methodologies
10-Fold Cross
Validation
10-Fold Cross
Validation
Number simulation runs
50 50
TABLE 1: The Overall System Setup Information.
9. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 9
6. RESULTS AND DISCUSSION
A number of experiments have been performed to evaluate the performance of the face
recognition system which is based on Feature Fusion and Classifier Ensemble. Sensitivity
analysis has also been performed to evaluate the performance of the system on reduced spatial
resolution images. The results obtained on the two selected benchmark databases are discussed
below.
6.1 Performance on the AT&T (ORL) Face Database
The performance of our proposed face recognition system on the AT&T (ORL) Face Database,
size 64X64, is shown in Figure 7. The figure shows that the classification accuracy of the system
improves as the number of base classifiers increases in the ensemble. The best performance of
99.78% classification accuracy has been achieved for feature fusion vector using PCA
Dimensionality of 50.
FIGURE 7: Performance on the AT&T (ORL) Database of size 64X64.
6.2 Performance on the Yale Face Database
The performance of our face recognition system on the Yale Face Database, size 64X64, is
shown in Figure 8. Once again, the performance of the classification system improves as the
number of base classifiers is increased in the ensemble system. However, compared to the AT&T
(ORL) Face Database, the performance of this database is slightly less with a 97.72%
classification accuracy. The PCA Dimensionality used for feature fusion is 50 in this case.
FIGURE 8: Performance on the Yale Database of size 64X64.
10. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 10
6.3 Sensitivity Analysis
We have further evaluated the performance of our Face Recognition System using face images
of lower spatial resolution than the ones discussed above. The results for AT&T (ORL) and Yale
databases at lower spatial resolution of 32X32 instead of 64X64 are shown in Figure 9 and Figure
10 respectively. A PCA dimensionality of size Dim=35 has been used for the low size images. We
have obtained accuracies of 99.05% and 96.21% for the AT&T (ORL) and the Yale databases
respectively. The results indicate a slight reduction in performance of the system for lower spatial
resolution images.
FIGURE 9: Performance on the AT&T (ORL) Database of size 32X32.
FIGURE 10: Performance on the Yale Database of size 32X32.
6.4 Summary of Results
The summary of results of our experiments and simulations is presented in Table 2 below.
11. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 11
Benchmark
Database
Image
Resolution
PCA
Dimensions
Classification
Accuracy Achieved
AT&T (ORL) 64X64 50 99.78%
Yale 64X64 50 97.72%
AT&T (ORL) 32X32 35 99.05%
Yale 32X32 35 96.21%
TABLE 2: Summary of Experimental Results.
It is clear from the table that highest classification accuracy of 99.78% has been achieved for the
AT&T (ORL) database of size 64X64 pixels. For the Yale database of similar spatial resolution,
we have obtained a classification accuracy of 97.72%. The better performance of our
classification system on the AT&T (ORL) database compared to the Yale one is due to the
challenging nature of the Yale database as it exhibits the PIE problem. The sensitivity analysis of
the system shows that our system is robust against any spatial resolution degradation as it
experiences a slight reduction in performance for lower resolution images of both databases.
7. COMPARISON OF RESULTS WITH STATE-OF-THE-ART TECHNIQUES
For the ORL database, researchers in the past have obtained classification rates ranging from
75.2% to 100% by employing various recognition techniques as discussed in Section 2 of this
paper. The 99.78% classification performance of our system for the AT&T (ORL) images of size
64x64 pixels is excellent compared to existing techniques since our system has been subjected
to an extreme validation strategy of 10-fold cross-validation along with the rigor of repeating the
experiments a number of times and then selecting the average classification accuracy of the runs.
This rigorous process highlights the robustness of our proposed technique along with the
generalization property of our system to unseen data. Our proposed system is thus superior to
any other system, which may have obtained a higher classification accuracy on a select few folds
without any substantial validation through additional simulation runs. Our system is also capable
of achieving 100% accuracy for some of the selected folds but we decided to determine a
detailed and comprehensive performance through extensive experimentation rather than
selecting a preferred fold with 100% accuracy. Also, the 64x64 size images used in our system
compared to the larger 112x92 sizes of the original AT&T (ORL) database represent the higher
robustness of our system compared to existing techniques that have been validated on higher
spatial resolution images. For the Yale database, the classification performances achieved by
previous researchers range from 84.24% to 99.5% accuracies. In this case too, the classification
accuracy of 97.72% achieved by our system is excellent taking into account the robust nature of
our system achieving this accuracy for reduced spatial resolution images of 64x64 size compared
to 320x243 size images in original Yale database.
Our proposed Feature Fusion and Classifier Ensemble technique is a valuable contribution to the
field of face recognition and it will have positive impact on the developments in this field.
Comparison of our results with the State-of-the-Art techniques for face recognition clearly
highlights the importance of the proposed technique. Simulations performed in this work have
achieved classification accuracies of 99.78% and 97.72% on the AT&T (ORL) and Yale
benchmark databases respectively, representing valuable contributions to the research effort in
this field.
8. CONCLUSIONS AND FUTURE WORK
In this paper, we have proposed a robust face recognition system based on Feature Fusion and
Classifier Ensemble techniques. We have obtained classification accuracy of 99.78% for the
AT&T (ORL) Database and 97.72% for the Yale Database using images of 64X64 spatial
resolution. Classification accuracy of 99.05% and 96.21% have been achieved for reduced
resolution AT&T (ORL) and Yale Databases respectively. Our face recognition system uses
Feature Fusion and an LDA based classification ensemble of 30 Base classifiers and 10-fold
cross validation. Feature vectors have been extracted from face images using the DWT
12. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 12
decomposition and HOG analysis. Dimensionality reduction and Feature Fusion have been
achieved using the PCA technique. The results achieved in this work have demonstrated the
effectiveness of our proposed system. It has been shown that using Feature Space Optimization
through the Feature Fusion Technique and Classifier Space Optimization through Classifier
Ensemble Technique has resulted in better classification performance instead of using
concatenated high dimensionality feature vectors and single classifier.
The performance of our classification system is influenced by a number of factors such as the
type of base classifiers, the number of base classifiers, the ensemble learning technique, type of
features, dimensionality of feature space etc. As future research directions, we plan to consider
various combinations of these factors in our research. We plan to consider other face image
databases, such as the Extended Yale database, and other learning schemes for the ensemble
system training. Also, future work would consider other types of base classifiers as members of
the ensemble.
9. ACKNOWLEDGEMENTS
This work would not have been possible without the great research support provided by the Arab
Open University (AOU) and the author greatly acknowledges this support.
10.REFERENCES
[1] A. K. Jain, A. Ross, and K. Nandakumar. Introduction to Biometrics. US, Springer, 2011.
[2] R. Jiang, S. Al-Madeed, A. Bouridane, D. Crookes, and A. Beghdadi, eds. Biometric Security
and Privacy: Opportunities & Challenges in The Big Data Era. US, Springer, 2017.
[3] M. Chihaoui, A. Elkefi, W. Bellil, and C. B. Amar. "A Survey of 2D Face Recognition
Techniques." Computers, vol. 5, no. 4, pp. 21, 2016.
[4] R. Jafri and H. R. Arabnia. "A survey of face recognition techniques." Jips, vol. 5, no. 2, pp.
41-68, 2009.
[5] T. Matthew, and A. Pentland. "Face recognition using eigenfaces." In Proc of IEEE
Computer Society Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-
591.
[6] T. Matthew, and A. Pentland. "Eigenfaces for recognition." Journal of cognitive
neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[7] G. Shakhnarovich, and M. Baback, Face recognition in subspaces. In Handbook of Face
Recognition, London, Springer, 2011, pp. 19-49.
[8] Y. Jian, D. Zhang, A. Frangi, and J. Yang. "Two-dimensional PCA: a new approach to
appearance-based face representation and recognition." IEEE transactions on pattern
analysis and machine intelligence, vol. 26, no. 1, pp. 131-137, 2004.
[9] M. Stewart, J. R. Movellan, and T. J. Sejnowski. "Face recognition by independent
component analysis." IEEE Transactions on neural networks, vol. 13, no. 6, pp. 1450-1464,
2002.
[10] B. Peter, J. P. Hespanha, and D. Kriegman. "Eigenfaces vs. fisherfaces: Recognition using
class specific linear projection." IEEE Transactions on pattern analysis and machine
intelligence, vol. 19, no. 7, pp. 711-720, 1997.
13. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 13
[11] H. Heiko. "Kernel PCA for novelty detection." Pattern Recognition, vol. 40, no. 3, pp. 863-
874, 2007.
[12] L. Juwei, K. N. Plataniotis, and A. N. Venetsanopoulos. "Face recognition using kernel direct
discriminant analysis algorithms." IEEE Transactions on Neural Networks, vol. 14, no. 1, pp.
117-126, 2003.
[13] B. R. Francis, and M. I. Jordan. "Kernel independent component analysis." Journal of
machine learning research, vol. 3, no. 1, pp. 1-48, Jul. 2002.
[14] Y. Ming-Hsuan. "Face recognition using extended isomap." In Proc International
Conference on Image Processing, 2002.
[15] L. Martin, J. C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R. P. Wurtz, and
W. Konen. "Distortion invariant object recognition in the dynamic link architecture." IEEE
Transactions on computers, vol. 42, no. 3, pp. 300-311, 1993.
[16] W. Laurenz, N. Krüger, N. Kuiger, and C. Von Der Malsburg. "Face recognition by elastic
bunch graph matching." IEEE Transactions on pattern analysis and machine intelligence,
vol. 19, no. 7, pp. 775-779, 1997.
[17] B. Roberto, and T. Poggio. "Face recognition: Features versus templates." IEEE
transactions on pattern analysis and machine intelligence, vol. 15, no. 10, pp. 1042-1052,
1993.
[18] L. T. Sing. "Image representation using 2D Gabor wavelets." IEEE Transactions on pattern
analysis and machine intelligence, vol. 18, no. 10, pp. 959-971, 1996.
[19] L. G. David. "Distinctive image features from scale-invariant keypoints." International journal
of computer vision, vol. 60, no. 2, pp. 91-110, 2004.
[20] T. Ahonen, A. Hadid, and M. Pietikäinen. "Face recognition with local binary patterns."
In Proc European conference on computer vision, 2004, pp. 469-481.
[21] N. V. Ara, and M. H. Hayes. "Face detection and recognition using hidden Markov models."
In Proc International Conference on Image Processing, 1998, pp. 141-145.
[22] A. Kar, P. Bhattacharjee, M. Nasipuri, D. K. Basu, M. Kundu. “High performance human face
recognition using gabor based pseudo hidden Markov model.” Int. J. Appl. Evol. Comput.,
2013, pp. 81-102.
[23] J. S. Kais. "Face recognition system using PCA and DCT in HMM." Int. J. Adv. Res.
Comput. Commun. Eng , vol. 4, pp. 13-18, 2015.
[24] M. Chihaoui, W. Bellil, A. Elkefi, and C. B. Amar. "Face recognition using HMM-LBP." Hybrid
Intelligent Systems, pp. 249-258, 2016.
[25] H. Di, J. Sun, X. Yang, D. Weng, and Y. Wang. "3d face analysis: Advances and
perspectives." In Proc Chinese Conference on Biometric Recognition, 2014, pp. 1-21.
[26] B. M. Alexander, M. M. Bronstein, R. Kimmel, and A. Spira. "3D face recognition without
facial surface reconstruction." Technion-Computer Science Department Technical Report,
2003.
14. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 14
[27] B. Stefano, A. Del Bimbo, and P. Pala. "3D face recognition using isogeodesic
stripes." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 12,
pp. 2162-2177, 2010.
[28] B. W. Kevin, K. Chang, and P. Flynn. "A survey of approaches and challenges in 3D and
multi-modal 3D+ 2D face recognition." Computer vision and image understanding, vol. 101,
no. 1, pp. 1-15, 2006.
[29] H. Di, Y. Wang, and Y. Wang. "A robust method for near infrared face recognition based on
extended local binary pattern." In Proc International Symposium on Visual Computing
Conference, 2007, pp. 437-446.
[30] G. Friedrich, and Y. Yeshurun. "Seeing people in the dark: face recognition in infrared
images." In Proc. International Workshop on Biologically Motivated Computer Vision, 2002,
pp. 348-359.
[31] J. A. Laszlo, H. Hashimoto, and T. Kubota. "Robust Facial Expression Recognition Using
Near Infrared Cameras." JACIII, vol. 16, no. 2, pp. 341-348, 2012.
[32] B. Stephen. "Deep learning and face recognition: the state of the art." In Proc. SPIE
Defense+ Security, 2015.
[33] N. Jiquan, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng. "Multimodal deep learning."
In Proc. The 28th international conference on machine learning (ICML-11), 2011, pp. 689-
696.
[34] S. Yi, Y. Chen, X. Wang, and X. Tang. "Deep learning face representation by joint
identification-verification." Advances in neural information processing systems, pp. 1988-
1996, 2014.
[35] P. M. Omkar, A. Vedaldi, and A. Zisserman. "Deep Face Recognition." BMVC, vol. 1, no. 3,
pp. 1-12, 2015.
[36] L. Yann, Y. Bengio, and G. Hinton. "Deep learning." Nature, vol. 521, no. 7553, pp. 436-444,
2015.
[37] T. Yaniv, M. Yang, M. Ranzato, and L. Wolf. "Deepface: Closing the gap to human-level
performance in face verification." In Proc. The IEEE Conference on Computer Vision and
Pattern Recognition, 2014, pp. 1701-1708.
[38] S. Florian, D. Kalenichenko, and J. Philbin. "Facenet: A unified embedding for face
recognition and clustering." In Proc. The IEEE Conference on Computer Vision and Pattern
Recognition, 2015, pp. 815-823.
[39] B. Amos, B. Ludwiczuk, and M. Satyanarayanan. “OpenFace: A general-purpose face
recognition library with mobile applications.” Technical report, CMU-CS-16-118, CMU School
of Computer Science, 2016.
[40] H. A. Khan, M. Al-Akaidi and R. W. Anwar. “Linear Discriminant Classifier Ensemble for
Face Recognition.” In Proc. of MESM’ 2014.
[41] The AT & T Database of Faces. Internet:
http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html [19 February 2017].
[42] The Yale Database. Internet: http://vision.ucsd.edu/content/yale-face-database [23 February
2017].
15. Hamayun A. Khan
Signal Processing: An International Journal (SPIJ), Volume (11) : Issue (1) : 2017 15
[43] N. Dalal and B. Triggs. “Histograms of oriented gradients for human detection.” In Proc IEEE
Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05),
2005, pp. 886-893.
[44] I. Daubechies. “Ten lectures on wavelets.” Society for industrial and applied mathematics,
1992.
[45] R. C. Gonzalez, and R. E. Woods. Digital Image Processing, Pearson International Edition,
2008.
[46] Z. Zhou, J. Wu, and W. Tang. “Ensembling neural networks: many could be better than all.”
Artificial Intelligence, vol. 137, no. 1-2, pp. 239-263, 2002.