NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITIONijsc
The word biometrics refers to the use of physiological or biological characteristics of human to recognize
and verify the identity of an individual. Face is one of the human biometrics for passive identification with
uniqueness and stability. In this manuscript we present a new face based biometric system based on neural
networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the
proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed
method to a variety of datasets and show the results.
Machine Learning in Material Characterizationijtsrd
Machine learning has shown great potential applications in material science. It is widely used in material design, corrosion detection, material screening, new material discovery, and other fields of materials science. The majority of ML approaches in materials science is based on artificial neural networks ANNs . The use of ML and related techniques for materials design, development, and characterization has matured to a main stream field. This paper focuses on the applications of machine learning strategies for material characterization. Matthew N. O. Sadiku | Guddi K. Suman | Sarhan M. Musa "Machine Learning in Material Characterization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46392.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/46392/machine-learning-in-material-characterization/matthew-n-o-sadiku
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.
NEURAL NETWORK BASED SUPERVISED SELF ORGANIZING MAPS FOR FACE RECOGNITIONijsc
The word biometrics refers to the use of physiological or biological characteristics of human to recognize
and verify the identity of an individual. Face is one of the human biometrics for passive identification with
uniqueness and stability. In this manuscript we present a new face based biometric system based on neural
networks supervised self organizing maps (SOM). We name our method named SOM-F. We show that the
proposed SOM-F method improves the performance and robustness of recognition. We apply the proposed
method to a variety of datasets and show the results.
Machine Learning in Material Characterizationijtsrd
Machine learning has shown great potential applications in material science. It is widely used in material design, corrosion detection, material screening, new material discovery, and other fields of materials science. The majority of ML approaches in materials science is based on artificial neural networks ANNs . The use of ML and related techniques for materials design, development, and characterization has matured to a main stream field. This paper focuses on the applications of machine learning strategies for material characterization. Matthew N. O. Sadiku | Guddi K. Suman | Sarhan M. Musa "Machine Learning in Material Characterization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-6 , October 2021, URL: https://www.ijtsrd.com/papers/ijtsrd46392.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/46392/machine-learning-in-material-characterization/matthew-n-o-sadiku
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
This paper describes for a robust face recognition system using skin segmentation technique. This paper addresses the problem of detecting faces in color images in the presence of various lighting conditions. In this paper the face is preprocessed using histogram equalization to avoid illumination problems and then is detected using skin segmentation method. The principal component analysis using neural network is used to recognize the extracted facial features.
Possibility fuzzy c means clustering for expression invariant face recognitionIJCI JOURNAL
Face being the most natural method of identification for humans is one of the most significant biometric
modalities and various methods to achieve efficient face recognition have been proposed. However the
changes in face owing to different expressions, pose, makeup, illumination, age bring about marked
variations in the facial image. These changes will inevitably occur and they can be controlled only till a
certain degree beyond which they are bound to happen and will affect the face thereby adversely impacting
the performance of any face recognition system. This paper proposes a strategy to improve the
classification methodology in face recognition by using Possibility Fuzzy C-Means Clustering (PFCM).
This clustering technique was used for face recognition due to its properties like outlier insensitivity which
make it a suitable candidate for use in designing such robust applications.PFCM is a hybridization of
Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) clustering algorithms. PFCM is a robust
clustering technique and is especially significant for its noise insensitivity. It has also resolved the
coincident clusters problem which is faced by other clustering techniques. Therefore the technique can also
be used to increase the overall robustness of a face recognition system and thereby increase its invariance
and make it a reliably usable biometric modality.
CONVOLUTIONAL NEURAL NETWORK BASED FEATURE EXTRACTION FOR IRIS RECOGNITION ijcsit
Iris is a powerful tool for reliable human identification. It has the potential to identify individuals with a
high degree of assurance. Extracting good features is the most significant step in the iris recognition
system. In the past, different features have been used to implement iris recognition system. Most of them are
depend on hand-crafted features designed by biometrics specialists. Due to the success of deep learning in
computer vision problems, the features learned by the Convolutional Neural Network (CNN) have gained
much attention to be applied for iris recognition system. In this paper, we evaluate the extracted learned
features from a pre-trained Convolutional Neural Network (Alex-Net Model) followed by a multi-class
Support Vector Machine (SVM) algorithm to perform classification. The performance of the proposed
system is investigated when extracting features from the segmented iris image and from the normalized iris
image. The proposed iris recognition system is tested on four public datasets IITD, iris databases CASIAIris-V1,
CASIA-Iris-thousand and, CASIA-Iris- V3 Interval. The system achieved excellent results with the
very high accuracy rate.
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%.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
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.
The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient’s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.
This thesis focuses on the problem of automatic skin lesion detection, particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach. For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region. For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm. Finally, this work performs a comparative evaluation of classification alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.
WCTFR : W RAPPING C URVELET T RANSFORM B ASED F ACE R ECOGNITIONcsandit
The recognition of a person based on biological fea
tures are efficient compared with traditional
knowledge based recognition system. In this paper w
e propose Wrapping Curvelet Transform
based Face Recognition (WCTFR). The Wrapping Curve
let Transform (WCT) is applied on
face images of database and test images to derive c
oefficients. The obtained coefficient matrix is
rearranged to form WCT features of each image. The
test image WCT features are compared
with database images using Euclidean Distance (ED)
to compute Equal Error Rate (EER) and
True Success Rate (TSR). The proposed algorithm wit
h WCT performs better than Curvelet
Transform algorithms used in [1], [10] and [11].
Feature Fusion and Classifier Ensemble Technique for Robust Face RecognitionCSCJournals
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.
A FACE RECOGNITION USING LINEAR-DIAGONAL BINARY GRAPH PATTERN FEATURE EXTRACT...ijfcstjournal
Face recognition is one the most interesting topic in the field in computer vision and image processing.
Face recognition is a processing system that recognizes and identifies individuals human by their faces.
Automatic face recognition is powerful way to provide, authorized access to control their system. Face
recognition has many challenging problems (like face pose, face expression variation, illumination
variation, face orientation and noise) in the field of image analysis and computer vision. This method is
work on feature extraction part of face recognition. New way to extract face feature using LD-BGP code
operator it is like LGS and LBP feature extraction operator. In our LD-BGP-code operator work in two
direction first linear then diagonal. In both direction, its create eight digits code to every pixel of image.
Means of these two directional are taken so that is cover all neighbor of center pixel. First linear direction,
only horizontal and vertical pixel are taken. Second diagonal direction only diagonal pixels taken. In
matching phase, we use Euclidean distance to match a face image. We perform the Linear and diagonal
directional operator method on face database ORL. We get accuracy 95.3 %. LD-BGP method also works
on different type image like illuminated and expression variation image.
Identifying Gender from Facial Parts Using Support Vector Machine ClassifierEditor IJCATR
Gender classification can be stated as inferring female or male from a collection of facial images. There exist different
methods for gender classification, such as gait, iris, hand shape and hair, it is probably better way to find out gender based on facial
features. In this paper SVM basic kernel function has been employed firstly to detect and classify the human gender Image into
two labels i.e. (1) male and (2) female. The gender classifier achieves over 96% accuracy.
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.
Facial recognition based on enhanced neural networkIAESIJAI
Accurate automatic face recognition (FR) has only become a practical goal of biometrics research in recent years. Detection and recognition are the primary steps for identifying faces in this research, and The Viola-Jones algorithm implements to discover faces in images. This paper presents a neural network solution called modify bidirectional associative memory (MBAM). The basic idea is to recognize the image of a human's face, extract the face image, enter it into the MBAM, and identify it. The output ID for the face image from the network should be similar to the ID for the image entered previously in the training phase. The tests have conducted using the suggested model using 100 images. Results show that FR accuracy is 100% for all images used, and the accuracy after adding noise is the proportions that differ between the images used according to the noise ratio. Recognition results for the mobile camera images were more satisfactory than those for the Face94 dataset.
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
This paper describes for a robust face recognition system using skin segmentation technique. This paper addresses the problem of detecting faces in color images in the presence of various lighting conditions. In this paper the face is preprocessed using histogram equalization to avoid illumination problems and then is detected using skin segmentation method. The principal component analysis using neural network is used to recognize the extracted facial features.
Possibility fuzzy c means clustering for expression invariant face recognitionIJCI JOURNAL
Face being the most natural method of identification for humans is one of the most significant biometric
modalities and various methods to achieve efficient face recognition have been proposed. However the
changes in face owing to different expressions, pose, makeup, illumination, age bring about marked
variations in the facial image. These changes will inevitably occur and they can be controlled only till a
certain degree beyond which they are bound to happen and will affect the face thereby adversely impacting
the performance of any face recognition system. This paper proposes a strategy to improve the
classification methodology in face recognition by using Possibility Fuzzy C-Means Clustering (PFCM).
This clustering technique was used for face recognition due to its properties like outlier insensitivity which
make it a suitable candidate for use in designing such robust applications.PFCM is a hybridization of
Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) clustering algorithms. PFCM is a robust
clustering technique and is especially significant for its noise insensitivity. It has also resolved the
coincident clusters problem which is faced by other clustering techniques. Therefore the technique can also
be used to increase the overall robustness of a face recognition system and thereby increase its invariance
and make it a reliably usable biometric modality.
CONVOLUTIONAL NEURAL NETWORK BASED FEATURE EXTRACTION FOR IRIS RECOGNITION ijcsit
Iris is a powerful tool for reliable human identification. It has the potential to identify individuals with a
high degree of assurance. Extracting good features is the most significant step in the iris recognition
system. In the past, different features have been used to implement iris recognition system. Most of them are
depend on hand-crafted features designed by biometrics specialists. Due to the success of deep learning in
computer vision problems, the features learned by the Convolutional Neural Network (CNN) have gained
much attention to be applied for iris recognition system. In this paper, we evaluate the extracted learned
features from a pre-trained Convolutional Neural Network (Alex-Net Model) followed by a multi-class
Support Vector Machine (SVM) algorithm to perform classification. The performance of the proposed
system is investigated when extracting features from the segmented iris image and from the normalized iris
image. The proposed iris recognition system is tested on four public datasets IITD, iris databases CASIAIris-V1,
CASIA-Iris-thousand and, CASIA-Iris- V3 Interval. The system achieved excellent results with the
very high accuracy rate.
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%.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
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.
The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient’s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.
This thesis focuses on the problem of automatic skin lesion detection, particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach. For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region. For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm. Finally, this work performs a comparative evaluation of classification alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.
WCTFR : W RAPPING C URVELET T RANSFORM B ASED F ACE R ECOGNITIONcsandit
The recognition of a person based on biological fea
tures are efficient compared with traditional
knowledge based recognition system. In this paper w
e propose Wrapping Curvelet Transform
based Face Recognition (WCTFR). The Wrapping Curve
let Transform (WCT) is applied on
face images of database and test images to derive c
oefficients. The obtained coefficient matrix is
rearranged to form WCT features of each image. The
test image WCT features are compared
with database images using Euclidean Distance (ED)
to compute Equal Error Rate (EER) and
True Success Rate (TSR). The proposed algorithm wit
h WCT performs better than Curvelet
Transform algorithms used in [1], [10] and [11].
Feature Fusion and Classifier Ensemble Technique for Robust Face RecognitionCSCJournals
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.
A FACE RECOGNITION USING LINEAR-DIAGONAL BINARY GRAPH PATTERN FEATURE EXTRACT...ijfcstjournal
Face recognition is one the most interesting topic in the field in computer vision and image processing.
Face recognition is a processing system that recognizes and identifies individuals human by their faces.
Automatic face recognition is powerful way to provide, authorized access to control their system. Face
recognition has many challenging problems (like face pose, face expression variation, illumination
variation, face orientation and noise) in the field of image analysis and computer vision. This method is
work on feature extraction part of face recognition. New way to extract face feature using LD-BGP code
operator it is like LGS and LBP feature extraction operator. In our LD-BGP-code operator work in two
direction first linear then diagonal. In both direction, its create eight digits code to every pixel of image.
Means of these two directional are taken so that is cover all neighbor of center pixel. First linear direction,
only horizontal and vertical pixel are taken. Second diagonal direction only diagonal pixels taken. In
matching phase, we use Euclidean distance to match a face image. We perform the Linear and diagonal
directional operator method on face database ORL. We get accuracy 95.3 %. LD-BGP method also works
on different type image like illuminated and expression variation image.
Identifying Gender from Facial Parts Using Support Vector Machine ClassifierEditor IJCATR
Gender classification can be stated as inferring female or male from a collection of facial images. There exist different
methods for gender classification, such as gait, iris, hand shape and hair, it is probably better way to find out gender based on facial
features. In this paper SVM basic kernel function has been employed firstly to detect and classify the human gender Image into
two labels i.e. (1) male and (2) female. The gender classifier achieves over 96% accuracy.
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.
Facial recognition based on enhanced neural networkIAESIJAI
Accurate automatic face recognition (FR) has only become a practical goal of biometrics research in recent years. Detection and recognition are the primary steps for identifying faces in this research, and The Viola-Jones algorithm implements to discover faces in images. This paper presents a neural network solution called modify bidirectional associative memory (MBAM). The basic idea is to recognize the image of a human's face, extract the face image, enter it into the MBAM, and identify it. The output ID for the face image from the network should be similar to the ID for the image entered previously in the training phase. The tests have conducted using the suggested model using 100 images. Results show that FR accuracy is 100% for all images used, and the accuracy after adding noise is the proportions that differ between the images used according to the noise ratio. Recognition results for the mobile camera images were more satisfactory than those for the Face94 dataset.
Face Recognition Based Intelligent Door Control Systemijtsrd
This paper presents the intelligent door control system based on face detection and recognition. This system can avoid the need to control by persons with the use of keys, security cards, password or pattern to open the door. The main objective is to develop a simple and fast recognition system for personal identification and face recognition to provide the security system. Face is a complex multidimensional structure and needs good computing techniques for recognition. The system is composed of two main parts face recognition and automatic door access control. It needs to detect the face before recognizing the face of the person. In face detection step, Viola Jones face detection algorithm is applied to detect the human face. Face recognition is implemented by using the Principal Component Analysis PCA and Neural Network. Image processing toolbox which is in MATLAB 2013a is used for the recognition process in this research. The PIC microcontroller is used to automatic door access control system by programming MikroC language. The door is opened automatically for the known person according to the result of verification in the MATLAB. On the other hand, the door remains closed for the unknown person. San San Naing | Thiri Oo Kywe | Ni Ni San Hlaing ""Face Recognition Based Intelligent Door Control System"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23893.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23893/face-recognition-based-intelligent-door-control-system/san-san-naing
Person identification based on facial biometrics in different lighting condit...IJECEIAES
Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.
Face detection and recognition has been prevalent with research scholars and diverse approaches have been
incorporated till date to serve purpose. The rampant advent of biometric analysis systems, which may be full body
scanners, or iris detection and recognition systems and the finger print recognition systems, and surveillance systems
deployed for safety and security purposes have contributed to inclination towards same. Advances has been made with
frontal view, lateral view of the face or using facial expressions such as anger, happiness and gloominess, still images
and video image to be used for detection and recognition. This led to newer methods for face detection and recognition
to be introduced in achieving accurate results and economically feasible and extremely secure. Techniques such as
Principal Component analysis (PCA), Independent component analysis (ICA), Linear Discriminant Analysis (LDA),
have been the predominant ones to be used. But with improvements needed in the previous approaches Neural Networks
based recognition was like boon to the industry. It not only enhanced the recognition but also the efficiency of
the process. Choosing Backpropagation as the learning method was clearly out of its efficiency to recognize non linear
faces with an acceptance ratio of more than 90% and execution time of only few seconds.
Social distance and face mask detector system exploiting transfer learningIJECEIAES
As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.
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.
Analysis of student sentiment during video class with multi-layer deep learni...IJECEIAES
The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the online class is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in this felids, the paper proposed the face recognition as sensitive learning task experiments on a large face databases demonstrate of the new feature. The researcher recommends that there's a needs to evaluate the previous studies and researches, especially on face recognition field and 3D, hopeful for advanced techniques and methods in the near future.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it is
appropriate in several real-world applications such as surveillance, face recognition, age synthesis, access
control, and electronic customer relationship management. Current deep learning-based methods have
displayed encouraging performance in age estimation field. Males and Females have a variable type of
appearance aging pattern; this results in age differently. This fact leads to assuming that using gender
information may improve the age estimator performance. We have proposed a novel model based on
Gender Classification. A Convolutional Neural Network (CNN) is used to get Gender Information, then
Bayesian Optimization is applied to this pre-trained CNN when fine-tuned for age estimation task.
Bayesian Optimization reduces the classification error on the validation set for the pre-trained model.
Extensive experiments are done to assess our proposed model on two data sets: FERET and FG-NET. The
experiments’ result indicates that using a pre-trained CNN containing Gender Information with Bayesian
Optimization outperforms the state of the arts on FERET and FG-NET data sets with a Mean Absolute
Error (MAE) of 1.2 and 2.67 respectively.
A detailed study on fraud analysis of international trade on ecpommerce platf...Smriti Tikoo
When items of utility or services are bought or sold via electronic exchange of money over an online crafted platform it is called an ecommerce transaction. The beauty of this platform is it allows users to access from the cossy comfort of their homes and offers a variety of items to choose from and hassle free shipping and timely deliveries.
Sometimes we try reasoning our actions way too much than necessary which can lead to depression and anxiety issues . One must learn to let go to breathe freely
![Smriti Tikoo et al, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May- 2016, pg. 288-295
© 2016, IJCSMC All Rights Reserved 289
rotation).Innumerable advances were made to cease the purpose of detecting and recognizing a facial image by the research scholars
across the globe.
Schneiderman and Kanade [1] used the feature based method utilizing the geometric facial features with belief networks for frontal,
profile and nonfrontal orientation of the images. L.Liao[2] incorporated Fast Fourier transform algorithms for the input face image
before applying recognition. The input images of face where Fast Fourier Transform transformed to whitened faces and facial
recognition was carried out using Principal Component analysis and Independent component analysis. The procedure of whitening
the faces facilitated better results via Principal Component Analysis and Independent Component Analysis .Lin [3] brought about
the Hausdroff distance for the recognition of faces; as a matter of fact the results were satisfactory for recognition and the
processing. Sven Loncaric and Zdrvako Liposcak [4] proposed a method of recognition for profile images based on the authentic
and morphological derived profile shapes, the source of information was taken from the line bounding the face and hair. Simon
Ceolin et. al[5] proposed the ideas from the statiscal shape analysis for constructing shape spaces encompassing the facial features
and gender and the resultant model for recognition. Ki Chung Chung [6] used the PCA and Gabor filter responses to recognize a
face. We propose to detect and recognize facial images using the Back propagation neural network(BPNN) .Viola Jones algorithm
has been used to detect a face and the features and recognition of face via BPNN .
II. PROBLEM DEFINITION
The advent of biometrics brought about a change in the way security of data or people and country was treated. Be it via
surveillance systems or the full body scans or the retina scans, hand scans, finger prints not only it has changed the security setup of
the country but it offers a sense of reliability to the users and system as well. It has invented ways to protect and preserve the
confidential data and guard the system (of any type) with help of human and computer interaction. It has brought about a great
combination of image processing and computer vision to light, that in a way have boosted the business for detection and recognition
systems. In this paper we propose a similar approach to detect and recognize a facial image using a BPNN with help of MATLAB
8.2. In the MATLAB we have worked using the neural network tool box, within which we have made use of the neural network
fitting tool to train and test the facial image at hand. This tool maps the numeric inputs to their targets. It helps in training, testing
and validating the data and evaluates the data on mean square error generated; performance plot and if regression needed plot for the
same data can also be put to use to make an inference regarding the performance of the process.
III. BACKPROPAGATION NEURAL NETWORK
It is a network which is consists of different interconnected layers. The term Back Propagation refers to the backward
propagation of errors in conjunction with optimization method i.e steepest descent. It calculates the local minima with respect
to the associated weights of the network. The weights are updated accordingly in order to reduce the local minima. Since this
network relies on a known target output for every input fed into the network, it is thereby a supervised learning method. This
algorithm is best understood by categorizing under two main phases namely:
Phase 1: Propagation:
1. Forward propagation: Input is fed through the network to generate propagation's output activations.
2. Backward propagation: A feedback network is formed by feeding the output as input in order to generate a difference
between actual and the target outputs.
Phase 2: Weight update:
1. Gradient of weight is a product of difference of outputs and input activation.
2. Subtract a ratio (percentage) of the gradient from the weight.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)