IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Face recognition across non uniform motion blur, illumination, and posePvrtechnologies Nellore
The document proposes a method for face recognition in the presence of non-uniform motion blur from hand-held cameras. It models a blurred face as a convex combination of geometrically transformed gallery images. It develops an algorithm using the assumption of sparse camera motion and an l1-norm constraint. The framework is extended to handle illumination variations by exploiting the bi-convex set of images from blurring and illumination changes. The method is also extended to account for pose variations and uses a multi-scale implementation for efficient computation and memory usage.
IRJET-Facial Expression Recognition using Efficient LBP and CNNIRJET Journal
This document presents a facial expression recognition system using efficient Local Binary Patterns (LBP) for feature extraction and a Convolutional Neural Network (CNN) for classification. LBP describes local texture features of images in a simple yet robust way. A CNN is used for classification as it can automatically extract both low-level and high-level features from images without needing separate feature extraction. The proposed system takes LBP feature maps as input to the CNN to improve its understanding and learning. When tested on the Cohn-Kanade dataset, the system achieved 90% accuracy in facial expression recognition.
An Enhanced Independent Component-Based Human Facial Expression Recognition ...أحلام انصارى
This document presents a facial expression recognition system that uses enhanced independent component analysis and fisher linear discriminant analysis (EICA-FLDA) for feature extraction from video frames, and hidden Markov models (HMM) for expression recognition. The system is tested on the Cohn-Kanade facial expression database and achieves a mean recognition rate of 93.23% for six universal expressions (anger, joy, sad, disgust, fear, surprise). Facial expression recognition has applications in human-computer interaction domains like online gaming.
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.
IRJET- An Effective System to Detect Face Drowsiness Status using Local F...IRJET Journal
This document presents a system to detect driver drowsiness using local facial features and a hierarchical decision-making structure. The system calculates four parameters - closed eyes, open mouth, blink rate, and yawning rate - using image processing techniques. These parameters are prioritized using a harmony search algorithm and then fed into a neural network to detect drowsiness. The system was tested on the YawDD dataset and achieved a detection rate of 79-82%, outperforming previous methods by 4-14%.
This document discusses using Local Binary Patterns (LBP) for facial expression recognition from images. It summarizes that LBP is used to extract features from facial regions that are divided into cells. Histograms of these LBP features are concatenated into a feature vector for each image. Support Vector Machines (SVM) are used to classify the images into six basic expressions based on these vectors, achieving better results than template matching or Linear Discriminant Analysis. The document also proposes a method for expression-invariant face recognition that classifies an input image, neutralizes it, then searches a neutral image dataset to find potential matches.
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.
This document describes a real-time facial expression recognition system that can handle low-resolution images and full head motion in real-world environments. The system uses background subtraction, head detection and pose estimation to analyze faces. It extracts location features like eye and mouth positions and shape features of the mouth region. A neural network then recognizes expressions like smile, anger and surprise from the features. The system aims to automatically recognize expressions in challenging real-world conditions like those in meetings, addressing limitations of prior systems.
Face recognition across non uniform motion blur, illumination, and posePvrtechnologies Nellore
The document proposes a method for face recognition in the presence of non-uniform motion blur from hand-held cameras. It models a blurred face as a convex combination of geometrically transformed gallery images. It develops an algorithm using the assumption of sparse camera motion and an l1-norm constraint. The framework is extended to handle illumination variations by exploiting the bi-convex set of images from blurring and illumination changes. The method is also extended to account for pose variations and uses a multi-scale implementation for efficient computation and memory usage.
IRJET-Facial Expression Recognition using Efficient LBP and CNNIRJET Journal
This document presents a facial expression recognition system using efficient Local Binary Patterns (LBP) for feature extraction and a Convolutional Neural Network (CNN) for classification. LBP describes local texture features of images in a simple yet robust way. A CNN is used for classification as it can automatically extract both low-level and high-level features from images without needing separate feature extraction. The proposed system takes LBP feature maps as input to the CNN to improve its understanding and learning. When tested on the Cohn-Kanade dataset, the system achieved 90% accuracy in facial expression recognition.
An Enhanced Independent Component-Based Human Facial Expression Recognition ...أحلام انصارى
This document presents a facial expression recognition system that uses enhanced independent component analysis and fisher linear discriminant analysis (EICA-FLDA) for feature extraction from video frames, and hidden Markov models (HMM) for expression recognition. The system is tested on the Cohn-Kanade facial expression database and achieves a mean recognition rate of 93.23% for six universal expressions (anger, joy, sad, disgust, fear, surprise). Facial expression recognition has applications in human-computer interaction domains like online gaming.
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.
IRJET- An Effective System to Detect Face Drowsiness Status using Local F...IRJET Journal
This document presents a system to detect driver drowsiness using local facial features and a hierarchical decision-making structure. The system calculates four parameters - closed eyes, open mouth, blink rate, and yawning rate - using image processing techniques. These parameters are prioritized using a harmony search algorithm and then fed into a neural network to detect drowsiness. The system was tested on the YawDD dataset and achieved a detection rate of 79-82%, outperforming previous methods by 4-14%.
This document discusses using Local Binary Patterns (LBP) for facial expression recognition from images. It summarizes that LBP is used to extract features from facial regions that are divided into cells. Histograms of these LBP features are concatenated into a feature vector for each image. Support Vector Machines (SVM) are used to classify the images into six basic expressions based on these vectors, achieving better results than template matching or Linear Discriminant Analysis. The document also proposes a method for expression-invariant face recognition that classifies an input image, neutralizes it, then searches a neutral image dataset to find potential matches.
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.
This document describes a real-time facial expression recognition system that can handle low-resolution images and full head motion in real-world environments. The system uses background subtraction, head detection and pose estimation to analyze faces. It extracts location features like eye and mouth positions and shape features of the mouth region. A neural network then recognizes expressions like smile, anger and surprise from the features. The system aims to automatically recognize expressions in challenging real-world conditions like those in meetings, addressing limitations of prior systems.
Three-dimensional multimodal models of objective classes are a great tool in modeling and recognition. The multimodal involuntary emotion recognition during a mentally challenged-based communication is presented. We have easily found the mentally disorder people without a doctor. The features are built upon the emotion, motion and frequency to identifying the percentage of mentally disorder peoples. Using Different categories of an image, video, audio and emotions can be discriminated. An image using an algorithms for classification is 3DMM (Three-dimensional morph able models) used to fit the model to images, and a framework for face emotion recognition. GPSO (Guided Particle Swarm Optimization) the emotion finding problem is basically an exploration problem, where at every point; we are pointed to recognize which of the thinkable emotions ensures the current facial expression denotes and GA (Genetic Algorithm) has the virtues of overflowing coding, and decoding, assigning complex information flexibly. GA is calculating the percentage of mental disorder. We proposed using different algorithm to identify the mentally challenged persons.
A Review on Face Detection under Occlusion by Facial AccessoriesIRJET Journal
This document reviews various methods for detecting faces that are partially occluded by accessories like sunglasses or scarves. It discusses approaches that divide the face into patches and use PCA to detect occluded regions. Other methods use particle filtering to track occluded objects over multiple frames, or detect occlusion through Gabor wavelets and SVM classification of facial components. More advanced techniques apply deep convolutional neural networks to simultaneously estimate positions of facial landmarks while being robust to occlusion, pose variations and illumination changes. The document concludes that occlusion detection is important for face recognition systems and that future work could aim to improve detection accuracy.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
Recognition of Partially Occluded Face Using Gradientface and Local Binary Pa...Win Yu
This document proposes a method for recognizing partially occluded faces using Gradientface and Local Binary Patterns (LBP). It first detects occluded regions using a MultiLayer Perceptron classifier, then applies Gradientface preprocessing to normalize for illumination before extracting LBP features from non-occluded regions for recognition. Experiments on the AR and ORL face databases show the method can effectively recognize faces with sunglasses and scarf occlusions.
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 summarizes a research paper on face recognition using Gabor features and PCA. It begins with an introduction to face recognition and discusses challenges like lighting, pose, and orientation. It then describes how the proposed system uses Gabor wavelets for preprocessing to reduce variations from pose, lighting, etc. Principal component analysis (PCA) is used to extract low dimensional and discriminating feature vectors from the preprocessed images. These feature vectors are then used for classification with k-nearest neighbors. The proposed system was tested on the Yale face database containing 100 images of 10 subjects with variable illumination and expressions.
A Novel Mathematical Based Method for Generating Virtual Samples from a Front...CSCJournals
This paper deals with one sample face recognition which is a new challenging problem in pattern recognition. In the proposed method, the frontal 2D face image of each person divided to some sub-regions. After computing the 3D shape of each sub-region, a fusion scheme is applied on sub-regions to create a total 3D shape for whole face image. Then, 2D face image is added to the corresponding 3D shape to construct 3D face image. Finally by rotating the 3D face image, virtual samples with different views are generated. Experimental results on ORL dataset using nearest neighbor as classifier reveal an improvement about 5% in recognition rate for one sample per person by enlarging training set using generated virtual samples. Compared with other related works, the proposed method has the following advantages: 1) only one single frontal face is required for face recognition and the outputs are virtual images with variant views for each individual 2) need only 3 key points of face (eyes and nose) 3) 3D shape estimation for generating virtual samples is fully automatic and faster than other 3D reconstruction approaches 4) it is fully mathematical with no training phase and the estimated 3D model is unique for each individual.
Happiness Expression Recognition at Different Age ConditionsEditor IJMTER
This document proposes a new robust subspace method called Proposed Euclidean Distance Score Level Fusion (PEDSLF) for recognizing happiness facial expressions with age variations. PEDSLF performs score level fusion of three subspace methods - PCA, ICA, and SVD. It normalizes the scores from each method and takes their maximum value for classification. The method is tested on two databases from FGNET and achieves recognition rates of 81.8% for ages 1-5 training and 10-15 testing, and 72% for ages 20-25 training and 30-35 testing. The results show PEDSLF performs better than the individual subspace methods for facial expression recognition with age variations.
A novel approach for performance parameter estimation of face recognition bas...IJMER
This document presents a novel approach for face recognition based on clustering, shape detection, and corner detection. The approach first clusters face key points and applies shape and corner detection methods to detect the face boundary and corners. It then performs both face identification and recognition on a large face database. The method achieves lower false acceptance rates, false rejection rates, and equal error rates compared to previous works, and also calculates recognition time. It provides a concise 3-sentence summary of the key aspects of the document.
This document reviews various techniques for iris segmentation in iris recognition systems. It discusses integrodifferential operator and Hough transform approaches, as well as the Masek, fuzzy clustering, and pulling and pushing methods. Each approach has advantages and disadvantages. The Masek method achieves circular iris and pupil localization but has lower accuracy and speed. Fuzzy clustering provides better segmentation for non-cooperative iris recognition but requires an extensive search. The pulling and pushing method aims to develop a more accurate and rapid iris segmentation algorithm.
Image deblurring based on spectral measures of whitenessijma
Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
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.
This document discusses facial expression recognition and the challenges that remain. It provides an overview of the current state-of-the-art techniques for facial expression recognition, which still struggle with accuracy when tested on naturalistic data rather than posed images. The document outlines a proposed pipeline for facial expression recognition that combines deep learning techniques for feature fusion and representation learning to help address these challenges and improve recognition accuracy on real-world data. Samples of datasets used for training and evaluating facial expression recognition systems are also presented.
Iris recognition for personal identification using lamstar neural networkijcsit
One of the promising biometric recognition method is Iris recognition. This is because the iris texture provides many features such as freckles, coronas, stripes, furrows, crypts, etc. Those features are unique for different people and distinguishable. Such unique features in the anatomical structure of the iris make it
possible the differentiation among individuals. So during last year’s huge number of people have been
trying to improve its performance. In this article first different common steps for the Iris recognition system
is explained. Then a special type of neural network is used for recognition part. Experimental results show high accuracy can be obtained especially when the primary steps are done well.
A Spectral Domain Local Feature Extraction Algorithm for Face RecognitionCSCJournals
In this paper, a spectral domain feature extraction algorithm for face recognition is proposed, which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal bands from the face image. In order to capture the local variations within these high-informative horizontal bands precisely, a feature selection algorithm based on two-dimensional discrete Fourier transform (2D-DFT) is proposed. Magnitudes corresponding to the dominant 2D-DFT coefficients are selected as features and shown to provide high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentations have been carried out upon standard face databases and the recognition performance is compared with some of the existing face recognition schemes. It is found that the proposed method offers not only computational savings but also a very high degree of recognition accuracy.
A Hybrid Approach to Recognize Facial Image using Feature Extraction MethodIRJET Journal
This document proposes a hybrid approach for facial image recognition using feature extraction and classification methods. It will use Principal Component Analysis (PCA) for feature extraction to reduce the dimensionality of feature vectors and select the most important features. This will be followed by Support Vector Machine (SVM) classification to classify facial images. PCA is applied to eigenfaces derived from facial training images to form a feature space. Test images are projected into this space and classified by SVM based on distance between their eigenvectors and stored eigenvectors. The approach aims to improve classification accuracy over other methods by combining effective feature extraction and classification.
1. The document proposes a hybrid approach to facial expression recognition that combines appearance features extracted using Local Directional Number descriptors with geometric features based on distances between facial landmark points.
2. The features are classified independently using SVMs and the scores are fused at the decision level using product rule fusion to identify facial expressions in images.
3. Experiments on the CK+ and JAFFE databases show the hybrid approach achieves better recognition rates than using appearance or geometric features individually.
The document summarizes and compares different methods for face recognition, including Eigenface, Line Edge Map (LEM), and other techniques. It provides descriptions of how each technique works, such as using eigenvectors to extract features for Eigenface. Experimental results show LEM achieves better accuracy than Eigenfaces for variations in lighting and size. While Eigenfaces struggles with size changes, LEM maintains high accuracy for different conditions. The document recommends future work combining techniques to maximize recognition accuracy.
This document presents a method for real-time facial expression analysis using principal component analysis (PCA). The method involves detecting faces, extracting expression features from the eye and mouth regions, applying PCA to extract texture features, and using a support vector machine classifier to classify expressions. The proposed approach was tested on a database of facial images with expressions categorized as happy, angry, disgust, sad, or neutral. PCA was used to select the most relevant eigenfaces and reduce the dimensionality of the feature space for more efficient classification of expressions in real-time.
This project includes two face recognition systems implemented with the help of Principal Component Analysis (PCA) and Morphological Shared-Weight Neural Network(MSNN).From these systems we will evaluate the performance of both the techniques and based on the accuracy achieved we determine which technique will be better for the face recognition
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.
The document proposes and evaluates four techniques for face recognition: PCA, LDA, KPCA, and KFA for feature extraction, followed by a radial basis neural network (RBF NN) for classification. It tests the techniques on the ORL database. For PCA and LDA, it extracts features from 80% of images for training an RBF NN model, then tests on the remaining 20% images. It finds the PCA+RBF NN achieves 91.66% accuracy at a target error of 0.01. The document also evaluates LDA, KPCA and KFA for feature extraction followed by RBF NN, comparing accuracies at different target error values.
Three-dimensional multimodal models of objective classes are a great tool in modeling and recognition. The multimodal involuntary emotion recognition during a mentally challenged-based communication is presented. We have easily found the mentally disorder people without a doctor. The features are built upon the emotion, motion and frequency to identifying the percentage of mentally disorder peoples. Using Different categories of an image, video, audio and emotions can be discriminated. An image using an algorithms for classification is 3DMM (Three-dimensional morph able models) used to fit the model to images, and a framework for face emotion recognition. GPSO (Guided Particle Swarm Optimization) the emotion finding problem is basically an exploration problem, where at every point; we are pointed to recognize which of the thinkable emotions ensures the current facial expression denotes and GA (Genetic Algorithm) has the virtues of overflowing coding, and decoding, assigning complex information flexibly. GA is calculating the percentage of mental disorder. We proposed using different algorithm to identify the mentally challenged persons.
A Review on Face Detection under Occlusion by Facial AccessoriesIRJET Journal
This document reviews various methods for detecting faces that are partially occluded by accessories like sunglasses or scarves. It discusses approaches that divide the face into patches and use PCA to detect occluded regions. Other methods use particle filtering to track occluded objects over multiple frames, or detect occlusion through Gabor wavelets and SVM classification of facial components. More advanced techniques apply deep convolutional neural networks to simultaneously estimate positions of facial landmarks while being robust to occlusion, pose variations and illumination changes. The document concludes that occlusion detection is important for face recognition systems and that future work could aim to improve detection accuracy.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
Recognition of Partially Occluded Face Using Gradientface and Local Binary Pa...Win Yu
This document proposes a method for recognizing partially occluded faces using Gradientface and Local Binary Patterns (LBP). It first detects occluded regions using a MultiLayer Perceptron classifier, then applies Gradientface preprocessing to normalize for illumination before extracting LBP features from non-occluded regions for recognition. Experiments on the AR and ORL face databases show the method can effectively recognize faces with sunglasses and scarf occlusions.
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 summarizes a research paper on face recognition using Gabor features and PCA. It begins with an introduction to face recognition and discusses challenges like lighting, pose, and orientation. It then describes how the proposed system uses Gabor wavelets for preprocessing to reduce variations from pose, lighting, etc. Principal component analysis (PCA) is used to extract low dimensional and discriminating feature vectors from the preprocessed images. These feature vectors are then used for classification with k-nearest neighbors. The proposed system was tested on the Yale face database containing 100 images of 10 subjects with variable illumination and expressions.
A Novel Mathematical Based Method for Generating Virtual Samples from a Front...CSCJournals
This paper deals with one sample face recognition which is a new challenging problem in pattern recognition. In the proposed method, the frontal 2D face image of each person divided to some sub-regions. After computing the 3D shape of each sub-region, a fusion scheme is applied on sub-regions to create a total 3D shape for whole face image. Then, 2D face image is added to the corresponding 3D shape to construct 3D face image. Finally by rotating the 3D face image, virtual samples with different views are generated. Experimental results on ORL dataset using nearest neighbor as classifier reveal an improvement about 5% in recognition rate for one sample per person by enlarging training set using generated virtual samples. Compared with other related works, the proposed method has the following advantages: 1) only one single frontal face is required for face recognition and the outputs are virtual images with variant views for each individual 2) need only 3 key points of face (eyes and nose) 3) 3D shape estimation for generating virtual samples is fully automatic and faster than other 3D reconstruction approaches 4) it is fully mathematical with no training phase and the estimated 3D model is unique for each individual.
Happiness Expression Recognition at Different Age ConditionsEditor IJMTER
This document proposes a new robust subspace method called Proposed Euclidean Distance Score Level Fusion (PEDSLF) for recognizing happiness facial expressions with age variations. PEDSLF performs score level fusion of three subspace methods - PCA, ICA, and SVD. It normalizes the scores from each method and takes their maximum value for classification. The method is tested on two databases from FGNET and achieves recognition rates of 81.8% for ages 1-5 training and 10-15 testing, and 72% for ages 20-25 training and 30-35 testing. The results show PEDSLF performs better than the individual subspace methods for facial expression recognition with age variations.
A novel approach for performance parameter estimation of face recognition bas...IJMER
This document presents a novel approach for face recognition based on clustering, shape detection, and corner detection. The approach first clusters face key points and applies shape and corner detection methods to detect the face boundary and corners. It then performs both face identification and recognition on a large face database. The method achieves lower false acceptance rates, false rejection rates, and equal error rates compared to previous works, and also calculates recognition time. It provides a concise 3-sentence summary of the key aspects of the document.
This document reviews various techniques for iris segmentation in iris recognition systems. It discusses integrodifferential operator and Hough transform approaches, as well as the Masek, fuzzy clustering, and pulling and pushing methods. Each approach has advantages and disadvantages. The Masek method achieves circular iris and pupil localization but has lower accuracy and speed. Fuzzy clustering provides better segmentation for non-cooperative iris recognition but requires an extensive search. The pulling and pushing method aims to develop a more accurate and rapid iris segmentation algorithm.
Image deblurring based on spectral measures of whitenessijma
Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
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.
This document discusses facial expression recognition and the challenges that remain. It provides an overview of the current state-of-the-art techniques for facial expression recognition, which still struggle with accuracy when tested on naturalistic data rather than posed images. The document outlines a proposed pipeline for facial expression recognition that combines deep learning techniques for feature fusion and representation learning to help address these challenges and improve recognition accuracy on real-world data. Samples of datasets used for training and evaluating facial expression recognition systems are also presented.
Iris recognition for personal identification using lamstar neural networkijcsit
One of the promising biometric recognition method is Iris recognition. This is because the iris texture provides many features such as freckles, coronas, stripes, furrows, crypts, etc. Those features are unique for different people and distinguishable. Such unique features in the anatomical structure of the iris make it
possible the differentiation among individuals. So during last year’s huge number of people have been
trying to improve its performance. In this article first different common steps for the Iris recognition system
is explained. Then a special type of neural network is used for recognition part. Experimental results show high accuracy can be obtained especially when the primary steps are done well.
A Spectral Domain Local Feature Extraction Algorithm for Face RecognitionCSCJournals
In this paper, a spectral domain feature extraction algorithm for face recognition is proposed, which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal bands from the face image. In order to capture the local variations within these high-informative horizontal bands precisely, a feature selection algorithm based on two-dimensional discrete Fourier transform (2D-DFT) is proposed. Magnitudes corresponding to the dominant 2D-DFT coefficients are selected as features and shown to provide high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentations have been carried out upon standard face databases and the recognition performance is compared with some of the existing face recognition schemes. It is found that the proposed method offers not only computational savings but also a very high degree of recognition accuracy.
A Hybrid Approach to Recognize Facial Image using Feature Extraction MethodIRJET Journal
This document proposes a hybrid approach for facial image recognition using feature extraction and classification methods. It will use Principal Component Analysis (PCA) for feature extraction to reduce the dimensionality of feature vectors and select the most important features. This will be followed by Support Vector Machine (SVM) classification to classify facial images. PCA is applied to eigenfaces derived from facial training images to form a feature space. Test images are projected into this space and classified by SVM based on distance between their eigenvectors and stored eigenvectors. The approach aims to improve classification accuracy over other methods by combining effective feature extraction and classification.
1. The document proposes a hybrid approach to facial expression recognition that combines appearance features extracted using Local Directional Number descriptors with geometric features based on distances between facial landmark points.
2. The features are classified independently using SVMs and the scores are fused at the decision level using product rule fusion to identify facial expressions in images.
3. Experiments on the CK+ and JAFFE databases show the hybrid approach achieves better recognition rates than using appearance or geometric features individually.
The document summarizes and compares different methods for face recognition, including Eigenface, Line Edge Map (LEM), and other techniques. It provides descriptions of how each technique works, such as using eigenvectors to extract features for Eigenface. Experimental results show LEM achieves better accuracy than Eigenfaces for variations in lighting and size. While Eigenfaces struggles with size changes, LEM maintains high accuracy for different conditions. The document recommends future work combining techniques to maximize recognition accuracy.
This document presents a method for real-time facial expression analysis using principal component analysis (PCA). The method involves detecting faces, extracting expression features from the eye and mouth regions, applying PCA to extract texture features, and using a support vector machine classifier to classify expressions. The proposed approach was tested on a database of facial images with expressions categorized as happy, angry, disgust, sad, or neutral. PCA was used to select the most relevant eigenfaces and reduce the dimensionality of the feature space for more efficient classification of expressions in real-time.
This project includes two face recognition systems implemented with the help of Principal Component Analysis (PCA) and Morphological Shared-Weight Neural Network(MSNN).From these systems we will evaluate the performance of both the techniques and based on the accuracy achieved we determine which technique will be better for the face recognition
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.
The document proposes and evaluates four techniques for face recognition: PCA, LDA, KPCA, and KFA for feature extraction, followed by a radial basis neural network (RBF NN) for classification. It tests the techniques on the ORL database. For PCA and LDA, it extracts features from 80% of images for training an RBF NN model, then tests on the remaining 20% images. It finds the PCA+RBF NN achieves 91.66% accuracy at a target error of 0.01. The document also evaluates LDA, KPCA and KFA for feature extraction followed by RBF NN, comparing accuracies at different target error values.
CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction System.
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSINGijiert bestjournal
Face recognition is a computer application technique for automatically identifying or
verifying a person from a digital image or a video frame source. To do this is by comparing
selected facial features from the digital image and a face dataset. It is basically used in
security systems and can be compared to other biometrics such as fingerprint recognition or
eye, iris recognition systems. The main limitation of the current face recognition system is
that they only detect straight faces looking at the camera. Separate versions of the system
could be trained for each head orientation, and the results can be combined using arbitration
methods similar to those presented here. In earlier work, the face position must be centerlight
position; any lighting effect will affect the system. Similarly the eyes of person must be
open and without glass.
REVIEW OF FACE DETECTION SYSTEMS BASED ARTIFICIAL NEURAL NETWORKS ALGORITHMSijma
Face detection is one of the most relevant applications of image processing and biometric systems.
Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition.
There is lack of literature surveys which give overview about the studies and researches related to the using
of ANN in face detection. Therefore, this research includes a general review of face detection studies and
systems which based on different ANN approaches and algorithms. The strengths and limitations of these
literature studies and systems were included also.
Review of face detection systems based artificial neural networks algorithmsijma
This document provides a review of face detection systems that are based on artificial neural network algorithms. It summarizes several studies that have used different types of neural networks for face detection, including:
1) Retinal connected neural networks and rotation invariant neural networks.
2) Principal component analysis combined with neural networks.
3) Convolutional neural networks, multilayer perceptrons, backpropagation neural networks, and polynomial neural networks.
4) Fast neural networks, evolutionary optimization of neural networks, and Gabor wavelet features with neural networks. Strengths and limitations of these different approaches are discussed.
IRJET- A Survey on Facial Expression Recognition Robust to Partial OcclusionIRJET Journal
This document summarizes various approaches for facial expression recognition that are robust to partial facial occlusions. It begins by introducing the topic and importance of facial expression recognition systems that can handle real-world scenarios involving partial occlusions. It then categorizes and reviews key approaches in the literature, including feature reconstruction based on PCA or RPCA, sparse coding approaches using SRC or MLESR, sub-space based methods using Gabor filters or LGBPHS, and statistical prediction models using Bayesian or tracking methods. The document focuses on studies that have researched expression recognition for facial images with partial occlusions.
Face Recognition System Using Local Ternary Pattern and Signed Number Multipl...inventionjournals
This paper presents a novel approach to face recognition. The task of face recognition is to verify a claimed identity by comparing a claimed image of the individual with other images belonging to the same individual/other individual in a database. The proposed method utilizes Local Ternary Pattern and signed bit multiplication to extract local features of a face. The image is divided into small non-overlapping windows. Processing is carried out on these windows to extract features. Test image’s features are compared with all the training images using Euclidean's distance. The image with lowest Euclidean distance is recognized as the true face image. If the distance between test and all training images is more than threshold then test image is considered as unrecognised image or match not found .The face recognition rate of proposed system is calculated by varying the number of images per person in training database
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
Research and Development of DSP-Based Face Recognition System for Robotic Reh...IJCSES Journal
This article describes the development of DSP as the core of the face recognition system, on the basis of
understanding the background, significance and current research situation at home and abroad of face
recognition issue, having a in-depth study to face detection, Image preprocessing, feature extraction face
facial structure, facial expression feature extraction, classification and other issues during face recognition
and have achieved research and development of DSP-based face recognition system for robotic
rehabilitation nursing beds. The system uses a fixed-point DSP TMS320DM642 as a central processing
unit, with a strong processing performance, high flexibility and programmability.
Implementation of Face Recognition in Cloud Vision Using Eigen FacesIJERA Editor
Cloud computing comes in several different forms and this article documents how service, Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The papers discuss a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed System is connection of two stages – Feature extraction using principle component analysis and recognition using the back propagation Network. This paper also discusses our work with the design and implementation of face recognition applications using our mobile-cloudlet-cloud architecture named MOCHA and its initial performance results. The dispute lies with how to performance task partitioning from mobile devices to cloud and distribute compute load among cloud servers to minimize the response time given diverse communication latencies and server compute powers
Comparative Analysis of Partial Occlusion Using Face Recognition TechniquesCSCJournals
This paper presents a comparison of partial occlusion using face recognition techniques that gives in which technique produce better result for total success rate. The partial occlusion of face recognition is especially useful for people where part of their face is scarred and defect thus need to be covered. Hence, either top part/eye region or bottom part of face will be recognized respectively. The partial face information are tested with Principle Component Analysis (PCA), Non-negative matrix factorization (NMF), Local NMF (LNMF) and Spatially Confined NMF (SFNMF). The comparative results show that the recognition rate of 95.17% with r = 80 by using SFNMF for bottom face region. On the other hand, eye region achieves 95.12% with r = 10 by using LNMF.
Facial expression recongnition Techniques, Database and Classifiers Rupinder Saini
This document discusses various techniques for facial expression recognition including eigenface approach, principal component analysis (PCA), Gabor wavelets, PCA with singular value decomposition, independent component analysis with PCA, local Gabor binary patterns, and support vector machines. It describes databases commonly used for facial expression recognition research and classifiers such as Euclidean distance, backpropagation neural networks, PCA, and linear discriminant analysis. The document concludes that combining multiple techniques can achieve more accurate facial expression recognition compared to individual techniques alone by extracting relevant features and evaluating results.
This document presents a hybrid framework for facial expression recognition that uses SVD, PCA, and SURF. It extracts features using PCA with SVD, classifies expressions with an SVM classifier, and performs emotion detection with regression and SURF features. The framework achieves 98.79% accuracy and 67.79% average recognition on a database of 50 images with 5 expressions. It provides a concise facial expression recognition system using a combination of dimensionality reduction, classification, and feature detection techniques.
The document discusses content-based face recognition using principal component analysis (PCA) and eigenfaces. It describes representing face images as linear combinations of eigenfaces in a face space defined by the eigenvectors of face images. The document outlines calculating eigenfaces from training images, representing and classifying new images using eigenfaces, and achieving 89% accuracy on a test dataset using 15 eigenfaces. It also discusses using neural networks and self-organizing maps for face recognition.
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.
COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT M...sipij
The physiological biometric trait face images are used to identify a person effectively. In this paper, we
propose compression based face recognition using transform domain features fused at matching level. The
2D images are converted into 1-D vectors using mean to compress number of pixels. The Fast Fourier
Transform (FFT) and Discrete Wavelet Transform (DWT) are used to extract features. The low and high
frequency coefficients of DWT are concatenated to obtained final DWT features. The performance
parameters are computed by comparing database and test image features of FFT and DWT using Euclidian
Distance (ED). The performance parameters of FFT and DWT are fused at matching level to obtain better
results. It is observed that the performance of proposed method is better than the existing methods.
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- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
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› ...
Artificial intelligence (AI) | Definitio
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Pose Invariant Face Recognition using Neuro-Fuzzy Approach
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. II (May – Jun. 2015), PP 20-27
www.iosrjournals.org
DOI: 10.9790/0661-17322027 www.iosrjournals.org 20 | Page
Pose Invariant Face Recognition using Neuro-Fuzzy Approach
Reecha Sharma1
, M.S Patterh1
1
Department of ECE Punjabi University Patiala, Punjab, India
Abstract : In this paper a pose invariant face recognition using neuro-fuzzy approach is proposed. Here
adaptive neuro fuzzy interface system (ANFIS) classifier is used as neuro-fuzzy approach for pose invariant face
recognition. In the proposed approach the preprocessing of image is done by using adaptive median filter. It
removes the salt pepper noise from the original images. From these denoised images features are extracted.
Here Principal component analysis (PCA) is used for extracting the features of an image under test. Then
ANFIS classifier is used for face recognition. PCA calculate the principal components and are used by ANFIS
for further process. Here in this paper combination of PCA and ANFIS is represented as PCA+ANFIS. In the
paper standard ORL face database is used for experimental results. The performance PCA+ANFIS with
LDA+ANFIS and ICA+ANFIS is analyzed and compared. From experimental results it is shown that
PCA+ANFIS outperforms than other two approaches. PCA+ANFIS is also compared by existing feed forward
neural network (FFBNN) approach. The results show that proposed approach gives better outputs in terms of
accuracy, sensitivity and specificity.
Keywords - Feature extraction, image denoising, image recognition, neural networks and feedforward neural
network.
I. Introduction
These days security is one of the most common and important issue. Passwords are easily hacked by
hackers. So for more security unique passwords or something which only users have for authentication are
necessary. Human traits are unique and more secure for identification. These days human authentication is done
by biometrics. Many biometric traits are used for this purpose. One of the most common reasons of using
biometric traits is that they are unique and can’t be stolen, share or reproduced. Most commonly used traits are
palm, iris, finger prints, voice and face. All traits except face need presence of an individual for authentication.
But in case for face trait images of a person can be use for this purpose. Human can easily recognize faces but
by computer vision machines and pattern recognition it is challenging task. Face recognition can hurriedly and
correctly recognize target persons when the conditions are favorable. Researchers find many challenges in face
recognition. The most common challenges are pose, illumination, occulation, expression etc. In this proposed
research paper a pose invariant system for face recognition has been studied.
The rest of paper is organized as follows: Section II explains a brief knowledge about related work.
Section III explains schematic of proposed neuro-fuzzy approach for pose invariant face recognition. Section IV
gives experimental results and discussion and in section V conclusion and future scope of proposed work
explained.
II. Related Work
There are two main broad categories of face recognition. They are template based or feature based. In
first category i.e. template based full face is considered for recognition purpose. In second category i.e. feature
based only common features are used. These features may be eyes, nose, mouth and ear etc. combination of
feature is also used for improving the recognition rate [1]. The eigenfaces or Principle Components are the set of
characteristic features of a face obtained by decomposing a face. Eigenface gives training phase. For recognition
test image is placed into subspace called face space and recognition is done by comparing the test image with
trained database in face space [2]. Neural network architecture is used for pose invariant face recognition. PCA
is used for feature extraction and neural network is used for recognition [3]. It seems to be very difficult for
human to recognize faces correctly when the illumination varies severely, since the same person appears to be
very much different. Back propagation neural network is used for illumination invariant face recognition. The
features of an image are extracted in eigenspace. Then illumination direction specific back propagation neural
network trains the extracted feature and then testing is down [4]. Radial basis function (RBF) neural classifier
reduces the problem of small training set of high dimensional [5]. Non linear face images give 90% of
acceptance ratio when back propagation neural network is combined with PCA [6]. Combination of SOM and
convolution network gives fast and automatic system for face recognition as compare to Eigen faces [7]. When
PCA combined with feed forward neural network PCA-NN gives improved recognition as compare to
traditional PCA. The same improvement is shown in LDA-NN [8]. FFNN improves the performance of face
2. Pose Invariant Face Recognition using Neuro-Fuzzy Approach
DOI: 10.9790/0661-17322027 www.iosrjournals.org 21 | Page
recognition as compare to Euclidean distance [9]. The novel architecture of the two-layer neural can recognize
human face with different views. It recognizes the identity of the person and the pose variation of a face at the
same time [10]. A combination of RBFNN and FHLA is used for face recognition. It provides faster training
with less number of neurons in hidden layer [11]. ANFIS and SVM fuse local and global features for face
verification. They fused together for better face recognition as compare to non adaptive and non fusion schemes
[12]. Linear Discriminant Analysis (LDA) is just like as eigenfaces of PCA. The only difference of LDA and
PCA is in the method of calculating the subspace. LDA maximize the ratio of the between class scatter matrix
and within class scatter matrix [13]. One hidden layer FFNN not only reduces the hidden units but also weights.
It gives better results as compare to BPNN [14]. Fusion of LDA and PCA give better results in face recognition
because they are intercorrelated to each other [15]. Independent component analysis (ICA) is a generality of
PCA. It separates the high order and second order moments. ICA is performed on images of face database by an
unsupervised learning algorithm derived from the optimal information transfer through sigmoidal neurons [16].
ICA, PCA and rough set theory combined to make a hybrid method for face recognition. ICA and PCA are used
for feature extraction and for recognition rough set rule based classifiers are used [17]. In this proposed paper
we are comparing PCA+ANFIS with LDA+ANFIS, ICA+ANFIS and also with FFBNN.
III. Schematic For Neuro-Fuzzy Approach
The proposed schematic for pose invariant face recognition using neuro-fuzzy approach is discussed in
this section. Here in the proposed approach PCA+ANFIS based pose invariant face recognition is done.
Proposed schematic for pose invariant face recognition using neuro-fuzzy approach have following steps.
1. The original images are taken from some standard face database. In our proposed schematic ORL face
database is used. The ORL face database images are grouped as test images and training images.
2. These images are preprocessed. Here hybrid filter called adaptive median filter is used for removing salt
and pepper noise from the original images.
3. These denoised images are given to PCA for feature extraction. Here principal components are calculated
and saved for further use.
4. These principal components are used by ANFIS a neuro-fuzzy classifier. The proposed ANFIS have five
layers. This is used for classification of images.
5. Here classified images are compared with predefined threshold value. If it greater than threshold value then
it is a recognized face otherwise not.
These steps are explained in detail in further sub sections.
3.1 Adaptive median filter
The adaptive median filter is used for preprocessor for noise removal. The noised images ),( srfd from ORL
face database are given as input to the adaptive median filter. These images are affected by the impulse and salt-
pepper noise. The output of adaptive median filter is noise free image. Fig.1 shows some samples of the noised
images from ORL face database. Fig.2 shows noise free output of adaptive median filter.
Figure 1: Sample of noised images from the ORL face database.
Figure 2: Denoised images after adaptive median filtering.
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The working of adaptive median filter is explained in following steps:
Step 1: Initialize the window w size zw .
Step 2: Check if the center pixel ),( srpcen within w is noisy. If the pixel ),( srpcen is noisy go to step 3.
Otherwise slide the window to the next pixel and repeat step 1.
Step 3: Arrange all pixels within the window w in an ascending order. Then calculate minimum ( ),(min srp ),
median ( ),( srpmed ), and maximum ( ),(max srp ) values.
Step 4: Calculate if ),( srpmed is noisy,
(i.e.) ),(),(),( maxmin srpsrpsrp med (1)
If the median value falls in the range of the minimum and maximum, it means that pixel is not a noisy pixel.
Then go to step 5. Otherwise ),( srpmed is a noisy pixel and then go to step 6.
Step 5: Here centre pixel of output image is replaced with ),( srpmed and go to step 8.
Step 6: In the step check whether all other pixels are noisy. If pixels are noisy then expend the window size by 2
and go to step 3. If not, go to step 7.
Step 7: Center pixel of noisy image is replaced with the noise free pixel which is the closest one of the median
pixel ),( srpmed .
Step 8: Reset window zw size. Center of window is changed to next pixel.
Step 9: Repeat all the steps until all pixels of an image are processed.
The table 1 shows the experimental results of noise removal filters. Average filter and Gaussian filter
are traditional filters used for noise removal but here in this work we are using adaptive median filter for noise
removal. The adaptive median filter is performed in spatial domain to find which pixels of an image are affected
by salt-pepper noise or impulse noise. In this each pixel is compared by neighbor pixels. The size of
neighborhood and threshold are adjustable. A pixel that is different from other neighborhood pixels is
considered as noise. These detected noise are replaced by median filter.
The main advantages of using adaptive median filter are as follow:
1. Remove impulse noise.
2. Smoothing of other noises
3. Reduces distortion of excessive thinning or thickening of boundaries.
In table 1, peak signal to noise ratio (PSNR) are calculated of some sample images. It is seen that
PSNR of adaptive median filter is better as compare to average filter and Gaussian filter. Here only few sample
images are taken for comparison.
Table 1 PSNR performance of Adaptive median filter, Average and Gaussian filter.
Images
PSNR
Adaptive
Median Filter
(in dB)
Average
Filter(in dB)
Gaussian
Filter (in
dB)
1 38.64 28.37 26.53
2 33.977 26.28 24.41
3 35.1861 26.81 26.02
4 34.54 26.19 25.52
5 33.96 26.68 25.08
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Figure 3: Illustration of PSNR of sample images for adaptive median filter, average filter and Gaussian filter.
Fig.3 shows the illustration of PSNR of sample images for adaptive median filter, average filter and
Gaussian filter. The five sample images are taken to represent PSNR graphically. Adaptive median filter shows
highs peak.
3.2 Principal components calculation using PCA
The noise free images which are obtained from adaptive median filter as output are used for feature
extraction. Here feature extraction is done by PCA. The principal components are calculated here. The common
steps for PCA are given below:
1. Taking the whole dataset ignoring the class labels.
2. Computing the d-dimensional mean vector.
3. Compute the covariance matrix.
4. Computing eigenvectors and corresponding eigenvalues.
5. Sorting the eigenvectors by decreasing eigenvalues
6. Transforming the samples onto the new subspace
7. The Eigenvectors with highest eigenvalues are principal components of an image.
pca(x1), pca(x2), pca(x3)…………… pca(xn) are principal components obtained from the PCA process.
These principal components are then passed into neuro-fuzzy based ANFIS classifier for classification process.
3.3 Neuro-fuzzy ANFIS classifier
The principal components pca(x1), pca(x2), pca(x3)…………… pca(xn) calculated from the PCA are
classified using neuro-fuzzy ANFIS classifier. The architecture of proposed neuro-fuzzy ANFIS consists of five
layers of nodes. From these five layers, the first and the fourth layers have adaptive nodes whereas the second,
third and fifth layers possess fixed nodes. The architecture of the ANFIS for pose invariant face recognition is
given in fig.4
Figure 4: Architecture of ANFIS for pose invariant face recognition.
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DOI: 10.9790/0661-17322027 www.iosrjournals.org 24 | Page
The learning process of ANFIS is carried out on the extracted PCA features such as Eigen vectors. The
Rule basis of the ANFIS is as follow:
If pca(x1) is Ai, pca(x1) is Bi and pca(xn) is Ci then
iniiii fxpcacxpcabxpcaaRules )()()( 21 (2)
Where pca(x1), pca(x2), pca(x3)…………… pca(xn) are the inputs. iA iB & iC are the fuzzy sets,
iRules is the output within the fuzzy region specified by the fuzzy rule, ia , ib , ic and if are the design
parameters that are determined by the training process.
Layer 1: Every node i in this layer is a square node with a node function.
))(()),(()),(( ,12,11,1 nCiBiAi xpcaxpcaOxpcaO ii
(3)
Usually
))(( 1xpcaiA
,
)(( 2xpcaiB
,
))(( nC xpcai
are chosen to be bell-shaped with maximum equal
to 1 and minimum equal to 0 and are defined as
i
ii
q
i
i
nC
BA
pca
ox
xpca
xpcaxpca
2
21
1
1
))((
))(())((
(4)
Where iii qpcao ,, are the parameter set. These are main parameters in this layer.
Layer-2: Layer 2 has circle nodes labeled by П. It multiplies the incoming signals and sends the product
output. For instance,
2,1
)),(())(())(( 21
,2
i
xpcaxpcaxpca
wtO
nCBA
ii
iii
(5)
Each node output represents the firing strength of a rule.
Layer-3: Every node in this layer is a circle node labeled N . The
th
i node calculates the ratio of the
th
i rules
firing strength to the sum of all rule’s firing strengths:
2,1),/( 21,3 iwtwtwtwtO iii
(6)
Layer-4: Every node i in this layer is a square node with a node function
2,1.,4 iRuleswtO iii
(7)
Where iwt is the output of layer 3 and ia , ib , ic , if are the parameter set. Parameters in this layer will be
referred to as consequent parameters.
Layer-5: The single node in this layer is a circle node labeled that computes the overall output as the
summation of all incoming signals:
i i
i ii
i
i
ii
wt
Ruleswt
RuleswtO ,5
(8)
21
2211
wtwt
RuleswtRuleswt
z
(9)
21 RuleswtRuleswtZ (10)
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Then the predefined threshold value and the result of the neural network Z is compared which is given in
Eq. (11).
Zrecognizednot
Zrecognized
result
,
,,
(11)
The neural network output Z greater than the threshold value ω means, the given input image is
recognized and Z less than the threshold value ω mean image is not recognized. Thus the ANFIS is well trained
using the score value obtained from PCA. The performance of the well trained ANFIS is tested by giving more
number of different pose images.
IV. Experimental Results And Discussions
The proposed schematic for pose invariant face recognition using neuro-fuzzy is implemented in
MATLAB. Here we compare the performance of filters for better noise removal. A sample image is denoised by
three filters. These are adaptive median filter, average filter and Gaussian filter. The results on ORL face
database shows that adaptive median filter gives high PSNR as compare to other two. Adaptive median filter
gives 38.64005 dB PSNR and on the other hand average filter gives 28.37dB and Gaussian filter gives 26.35dB.
Here results of PSNR shows that choice of adaptive median filter for noise removal is better as compare to other
filters.
Accordingly the denoised images acquired from the adaptive median filter are used to calculate the
principal components utilizing the PCA based calculation. The principal components in this way acquired from
the PCA are given as the input to the ANFIS classifier. More number of face images is used to analyze the
performance of the proposed face recognition system using different statistical performance measures.
The face images from ORL database are utilized to analyze the performance of neuro-fuzzy based
PCA+ANFIS approach with the ICA+ANFIS and LDA+ANFIS approach. The comparison results of the
proposed PCA+ANFIS, ICA+AFIS and LDA+ANFIS approach are shown in the table 1.
Table 2: Demonstrate the Performance comparison of the proposed neuro-fuzzy based PCA+ANFIS,
ICA+ANFIS and LDA+ANFIS.
Measures Proposed
PCA+ANFIS
(%)
ICA+ANFIS
(%)
LDA+ANFIS
(%)
Accuracy 96.66 71.3 68
Sensitivity 97.29 72.8 64.83
Specificity 96.05 71.2 72.88
In table 2 the accuracy of the proposed PCA+ANFIS approach is 96.66% but the ICA+ANFIS and
LDA+ANFIS approaches have offer only 71.3%, 68% of accuracy respectively. Similarly the sensitivity and
specificity of the proposed PCA+ANFIS approach is 97.29% and 96.05% but the ICA+ANFIS and
LDA+ANFIS approach give 72.8%, 64.83% of sensitivity and 71.2%, 72.88% of specificity respectively. Hence
from the table it can be seen that proposed approach recognizes the image more accurately.
Moreover proposed PCA+ANFIS is also compared with the existing FFBNN technique in terms of
sensitivity, specificity and accuracy and many more measures. In this approach the measurements are taken in
terms of true positive (TP) is correctly identified images by the approaches used for comparison, true negative
(TN) is correctly rejected images, false positive (FP) is incorrectly identify images and false negative (FN) is
incorrectly rejected images.
Here accuracy as also called true positive rate (TPR) and is given by
(12)
Specificity or true negative rate (TNR) is given by
(13)
False positive rate (FPR) is given by
(14)
Positive predictive value (PPV) is given by
(15)
Negative predictive value (NPV) is given by
(16)
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The results are shown below in table 3.
Table 3: Illustrates the performance measures of the proposed PCA+ANFIS approach and the existing FFBNN
approach in terms of accuracy, sensitivity and specificity
Measures Proposed
PCA+ANFIS
Existing
FFBNN
Accuracy 0.9666 0.8666
Sensitivity 0.9729 0.8481
Specificity 0.9605 0.8873
FPR 0.0394 0.1126
PPV 0.96 0.8933
NPV 0.9733 0.84
FDR 0.04 0.106
MCC 0.9334 0.7343
From the table 3 it can be seen that the proposed PCA+ANFIS has given accuracy of 0.9666 but the
existing FFBNN has given accuracy of only 0.8666. Similarly the sensitivity and the specificity of our proposed
method are higher than the existing FFBNN. Fig.8 shows the illustrations of all measurement of PCA+ANFIS
and existing FFBNN.
Figure 8: Illustration of comparison of accuracies of proposed neuro-fuzzy based PCA+ANFIS approach with
ICA+ANFIS and LDA+ANFIS.
V. Conclusions
In this paper pose invariant face recognition using neuro-fuzzy approach is proposed. First the images
under test are denoised by using adaptive median filter and its performance is compared with average filter and
Gaussian filter. From the comparative result it has been found that adaptive median filter performs better as
compared to Average and Gaussian filter. PCA is used for feature extraction and ANFIS is used for face
recognition. The performance of the proposed PCA+ANFIS is compared with ICA+ANFIS and LDA+ANFIS.
From the comparative results it has been found that PCA+ANFIS perform better than ICA+ANFIS and
LDA+ANFIS. For example the proposed PCA+ANFIS give accuracy of 96.66% as compared to ICA+ANFIS
which gives 71.3% and LDA+ANFIS which gives 68%. Proposed PCA+ANFIS technique also performs better
than FFBNN. It has been concluded that PCA+ANFIS set up can be used for face recognition with better
accuracy. The proposed technique can further improve in future for other parameters like illumination,
occulation or age.
Acknowledgements
Author would like to express greatest gratitude to Dr. M.S Patterh Department of ECE, Punjabi
University for his continuous support for the paper from initial advice & contacts in the early stages of
conceptual inception & through ongoing advice & encouragement to this day. Author would also like to thank
the anonymous reviewers for their constructive comments. Author would like to thank AT&T Laboratories
Cambridge for providing the ORL face database.
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References
[1]. Brunelli Roberto And Tomaso Poggio, "Face Recognition: Features Versus Templates," IEEE Transactions On Pattern Analysis
And Machine Intelligence, Vol. 15, No. 10, Pp. 1042-1052, 1993.
[2]. Turk Matthew And Alex Pentland, "Eigenface For Recognition," Journal Of Cognitive Neuroscience, Vol. 3, No. 1, Pp. 71-86,
1991.
[3]. Huang, Fu Jie, Zhihua Zhou, Hong-Jiang Zhang, And Tsuhan Chen, "Pose Invariant Face Recognition," In Fourth IEEE
International Conference On Automatic Face And Gesture Recognition, 2000, Pp. 245-250.
[4]. Wu-Jun Li, Chong-Jun Wan, Dian-Xiang Xu, And Shi-Fu Chen, "Illumination Invariant Face Recognition Based On Neural
Network Ensemble," In Proceedings Of The 16th IEEE International Conference On Tools With Artificial Intelligence (ICTAI
2004), 2004.
[5]. Ermeng Joo, Wu Shiqian, Lu Juwei, And Toh Hock Lye, "Face Recognition With Radial Basis Function (RBF) Neural Networks,"
Neural Networks, IEEE Transactions On, Vol. 13, No. 3, Pp. 697-710, 2002.
[6]. Mohammod Abul Kashem, Md. Nasim Akhter, Shamim Ahmed, And Md. Mahbub Alam, "Face Recognition System Based On
Principal Component Analysis (PCA) And Back Propagation Neural Network (BPNN) ," International Journal Of Scientific &
Engineering Research Volume 2, Issue 6, June-2011, Vol. 2, No. 6, Pp. 1-10, June 2011.
[7]. Lawrence Steve, Giles C Lee, Tsoi Ah Chung, And Back Andrew D, "Face Recognition: A Convolutional Neural-Network
Approach," Neural Networks, IEEE Transactions On, Vol. 8, No. 1, Pp. 98-113, 1997.
[8]. Alaa Eleyan And Hasan Demirel, "PCA And LDA Based Face Recognition Using Feedforward Neural Network Classifier,"
Multimedia Content Representation, Classification And Security, Pp. 199-206, 2006.
[9]. Alaa And Demirel, Hasan Eleyan, "Face Recognition System Based On PCA And Feedforward Neural Networks," In
Computational Intelligence And Bioinspired Systems, Pp. 935--942, 2005.
[10]. Huang Fu Jie, Zhihua Zhou, Hong-Jiang Zhang, And Tsuhan Chen, "Pose Invariant Face Recognition," In In Automatic Face And
Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference On, 2000, Pp. 245-250.
[11]. Haddadnia Javad, Faez Karim, And Ahmadi Majid, "A Fuzzy Hybrid Learning Algorithm For Radial Basis Function Neural
Network With Application In Human Face Recognition," Pattern Recognition, Vol. 36, No. 5, Pp. 1187-1202, 2003.
[12]. Fang, Yachun, Tieniu Tan, And Yunhong Wang, "Fusion Of Global And Local Features For Face Verification," In Pattern
Recognition, 2002. Proceedings. 16th International Conference On, Vol. 2, 2002, Pp. 382--385.
[13]. Eleyan Alaa And Demirel Hasan, Pca And LDA Based Neural Networks For Human Face Recognition.: INTECH Open Access
Publisher, 2007.
[14]. Ma Liying And Khorasani Khashayar, "Facial Expression Recognition Using Constructive Feedforward Neural Networks,"
Systems, Man, And Cybernetics, Part B: Cybernetics, IEEE Transactions On, Vol. 34, No. 3, Pp. 1588--1595, 2004.
[15]. Marcialis Gian Luca And Fabio Roli, "Fusion Of LDA And PCA For Face Verification," In Biometric Authentication, Pp. 30-37,
2002.
[16]. Bartlett Marian Stewart, "Independent Component Representations For Face Recognition," In Face Image Analysis By
Unsupervised Learning Springer US, Pp. 39-67, 2001.
[17]. Swiniarski, Roman W, And Andrzej Skowron, "Independent Component Analysis, Principal Component Analysis And Rough Sets
In Face Recognition," Transactions On Rough Sets I. Springer Berlin Heidelberg, Pp. 392-404, 2004.