Nowadays face recognition system usage is increasing day by day to provide better security mechanism . But with the face recognition system , there are some spoofing methods also attached using which the system can be befooled easily . The performance of face recognition systems tends to decrease when images are affected by illumination . Feature extraction is one of the main steps of a face recognition process , where it is possible to alleviate the illumination effects on face images . In order to increase the accuracy of recognition tasks , different methods for obtaining illumination invariant features have been developed . Furthermore , the PR can be executed parallelly . We applied the PR scheme to a large scale ANN for face image recognition . Procuring a low-dimensional feature with aggrandized discriminatory power is of pre-eminent importance to face recognition . In this paper a face recognition analysis is suggested based on Hybrid Gaborlet and Kernel Fisher Analysis . Flustered SVD ( Fsvd ) focuses at deriving an illumination invariant image . First , face discriminant features are obtained from the computation of PCA or interpolation methods as well as Fishers methods . Then , face recognition analysis is carried out by six distance classifiers and their combination based on majority poll and other common rules . Finally , computation results are discussed . Research of human face recognition is an important topic in the area of pattern recognition and artificial intelligence , and it has very broad application prospects . The extraction of face features is an important part in the process of face automatic recognition . It is essential to introduce the extraction of face features based on integral projection , singular value decomposition and wavelet and so on . A preliminary study of face-based recognition on a transgender dataset . In this work we investigate a truly novel and extremely unique biometric problem : face-based recognition for transgender persons . A transgender person is someone who under goes a gender transformation via hormone replacement therapy ; that is , a male becomes a female by suppressing natural testosterone production and exogenously increasing estrogen . It is widely acknowledged that the face identification has played a significant role in the observation system as it does n’t need the object ’s assistance . The definite advantages of face based recognition over other biometrics are distinctiveness and response . As human face is an active object having a high degree of unpredictability in its manifestation , that makes face detection a hard problem in computer vision . Face Recognition.- Super-Resolved Faces for Improved Face Recognition from Surveillance Video.- Face Detection Based on Multi-Block LBP Representation.- Color Face Tensor Factorization and Slicing for Illumination-Robust Recognition.- Robust Real-Time Face Detection Using Face Certainty Map.- Poster I.- Motion Compensation for Face Recognition Based on Active Differential Imaging.- Face Recognition with Local Gabor Textons.- Speaker Verification with Adaptive Spectral Subband Centroids.- Similarity Rank Correlation for Face Recognition Under Unenrolled Pose.- Feature Correlation Filter for Face Recognition.- Face Recognition by Discriminant Analysis with Gabor Tensor Representation.- Fingerprint Enhancement Based on Discrete Cosine Transform.- Biometric Template Classification : A Case Study in Iris Textures.- Protecting Biometric Templates with Image Watermarking Techniques.- Factorial Hidden Markov Models for Gait Recognition.- A Robust Fingerprint Matching Approach : Growing and Fusing of Local Structures.- Automatic Facial Pose Determination of 3D Range Data for Face Model and Expression Identification.- SVDD-Based Illumination Compensation for Face Recognition.- Keypoint Identification and Feature-Based 3D Face Recognition.- Fusion of Near Infrared Face and Iris Biometrics.- Multi-Eigenspace Learning for Video-Based Face Recognition.- Error-Rate Based Biometrics Oral - I.- Learning Personal Specific Facial Dynamics for Face Recognition from Videos.- A New Probabilistic Model for Recognizing Signs with Systematic Modulations.- Model-Based Stereo with Occlusions.- View Invariant Head Recognition by Hybrid PCA Based Reconstruction.- Poster - I.- Person-Independent Monocular Tracking of Face and Facial Actions with Multilinear Models.- Automatic Facial Expression Recognition Using Boosted Discriminatory Classifiers.- Generating Body Surface Deformation Using Level Set Method.- Patch-Based Pose Inference with a Mixture of Density Estimators.- Integrating Multiple Visual Cues for Robust Real-Time 3D Face Tracking.- Model-Assisted 3D Face Reconstruction from Video.- Human Perambulation as a Self Calibrating Biometric.- Oral - II.- Detecting , Localizing and Classifying Visual Traits from Arbitrary Viewpoints Using Probabilistic Local Feature Modeling.- Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions.- Structured Ordinal Features for Appearance-Based Object Representation.- SODA-Boosting and Its Application to Gender Recognition.- Poster - II.- Single Image Subspace for Face Recognition.- Human Face Processing with 1.5D Models.- Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition.- A Unified Framework of Subspace and Distance Metric Learning for Face Recognition.- Face Recognition Based on Pose-Variant Image Synthesis and Multi-level Multi-feature Fusion.- Towards Pose-Invariant 2D Face Classification for Surveillance.- Robust Face Recognition Strategies Using Feed-Forward Architectures and Parts . We introduce a fast , robust , accurate eye localization algorithm . Detecting and normalizing human faces from live video streams is the first crucial step in a face verification/recognition system . The accuracy and robustness affect the performance of the following face registration and classification . We perform thorough analysis of various probability calibration techniques with the help of methods inspired from meteorology . We test our methods on Labeled Faces in the Wild dataset and show suitability of the proposed approach for face verification/recognition . Face restoration from low resolution and noise is important for applications of face analysis recognition . However , most existing face restoration models omit the multiple scale issues in the face restoration problem , which is still not well solved in the research area . Single chip solutions with focus on reconfiguration , low area , and high speed are desirable for such applications . This thesis reports hardware-directed algorithms and array-type Very Large Scale Integrated ( VLSI ) architectures for navigation and face-based human recognition . The key contributions are listed below . Transfer learning using supervised features has been successful in several computer vision applications . Striking examples include face recognition and visual question answering , where image features trained on ImageNet and word embeddings trained on large unsupervised corpora are combined . In contrast , most approaches for sentence representation learning are unsupervised , arguably because the NLP community has not yet found the best supervised task for embedding the semantics of a whole sentence . Convolutional neural networks ( CNN ) date back decades [ reference ] and deep CNNs have recently shown an explosive popularity partially due to its success in image classification [ reference ] , [ reference ] . They have also been successfully applied to other computer vision fields , such as object detection [ reference ] , [ reference ] , [ reference ] , face recognition [ reference ] , and pedestrian detection [ reference ] . Several factors are of central importance in this progress : ( i ) the efficient training implementation on modern powerful GPUs [ reference ] , ( ii ) the proposal of the Rectified Linear Unit ( ReLU ) [ reference ] which makes convergence much faster while still presents good quality [ reference ] , and ( iii ) the easy access to an abundance of data ( like ImageNet [ reference ] ) for training larger models . 2D Face Alignment . Face alignment is a critical step for any facial analysis task such as face recognition . With enhancement in the modeling , we hope to improve this task ( Fig . First , there are often one or two valid scales in the image , and the scale - forecast network can automatically select some particular scales , and ignore all the other invalid ones in the multi - scale test stage ; second , the input of LRN descends from the output of RSA to predict feature maps at smaller scales ; it is not necessary to compute feature maps of multiple scales in a multi - shot manner as RPN _ m does . subsection : Face Detection Fig . [ reference ] illustrates this augmentation process . Specifically , following face detection and landmark detection , we transform detected bounding boxes and their detected facial landmarks using a number of simple in - plane transformations . The parameters for these transformations are selected randomly from fixed distributions ( Table . By classifying face images into thousands or even millions of identities , the last hidden layer forms features highly discriminative to identities . The second is supervising deep neural networks with both face identification and verification tasks . The verification task minimizes the distance between features of the same identity , and decreases intra - personal variations . These representations have shown strong performance for recognition of imagery and video in - the - wild in unconstrained datasets , with recent approaches demonstrating capabilities that exceed human performance on the well known Labeled Faces in the Wild dataset . The problem of face recognition may be described in terms of face verification and face identification . Face verification involves computing a one - to - one similarity between a probe image and a reference image , to determine if two image observations are of the same subject . document : Orthogonal Deep Features Decomposition for Age - Invariant Face Recognition As facial appearance is subject to significant intra - class variations caused by the aging process over time , age - invariant face recognition ( AIFR ) remains a major challenge in face recognition community . To reduce the intra - class discrepancy caused by the aging , in this paper we propose a novel approach ( namely , Orthogonal Embedding CNNs , or OE - CNNs ) to learn the age - invariant deep face features . This way , the aggregation module can handle an arbitrary set of image or video faces without temporal information ( e.g. that collected from different Internet locations ) . Third , the module should be adaptive to the input faces and has parameters trainable through supervised learning in a standard face recognition training task . Our solution is inspired by the memory attention mechanism described in [ reference ][ reference ][ reference ] . [ reference ] ( a ) ) . Lastly , in comparison to human face verification , car verification , which targets at verifying whether two cars belong to the same model , is an interesting and under - researched problem . The unconstrained viewpoints make car verification arguably more challenging than traditional face verification . When lighting condition changes , the appearance of a face varies intensely . In this paper , an illumination normalization method for face verification under various lighting conditions is presented . In the proposed approach , 2D wavelet is applied on the Yale database B and XM2VTS human face databases for illumination normalization in face verification . In recent , face verification technique is applied to mobile devices to authenticate owner of them . Face verification on mobile devices needs to be considered with both performance and computational cost which is trade-off . In this paper , we propose lightweight network architectures and fusion methods for efficient face verification task on mobile devices . The ELM advocates the convergence of machine learning and biological learning for pervasive learning and intelligence and has been extensively researched in widespread applications . Nonetheless , till date , none of the work of ELM has proved its competency in tackling face verification problem . Hence , in this paper , we are going to probe for the first time the feasibility of ELM-based network in handling the face verification task . In the preliminary analysis , feature vectors extracted from different views of the same Lytro picture are proved different enough to provide complementary information beneficial for face recognition purpose . Starting from a set of multiple views for each data , face verification problem is tackled and results are compared with those achieved with classical 2D images simulated using a single view , i.e. the central one . Two experiments are described and , in both cases , the presented method shows superior performances than standard algorithms adopted by classical imaging sensors . Priority is given to regions which signal high intensity variance , thereby allowing the most probable eye regions to be selected . On detecting a mouth and two eyes , a face verification step based on Eigenface theory is applied to a normalized search space in the image relative to the distance between the eye feature points . Facial features such as eyes , nose and mouth are automatically detected based on properties of the associated image regions . On detecting a mouth , a nose and two eyes , a face verification step based on Eigen face theory is applied to a normalized search space in the image relative to the distance between the eye feature points . The experiments were carried out on test images taken from the internet and various other randomly selected sources . The underlying assumption in identification is that the gallery always has sufficient samples per subject to linearly reconstruct a query image . Unfortunately , such assumption is easily violated in the more challenging and realistic face verification scenario . A verification algorithm is required to determine if two faces ( where one or both have not been seen before ) belong to the same person , while explicitly taking into account the possibility of impostor attacks . The underlying assumption in SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class . Unfortunately , such assumption is easily violated in the more challenging face verification scenario , where an algorithm is required to determine if two faces ( where one or both have not been seen before ) belong to the same person . In this paper , we first discuss why previous attempts with SR might not be applicable to verification problems . Video-replay spoofing is a serious threat for face authentication/verification system . A real-time approach to detect video-replay attack using a generic webcamera is presented . Face and voice biometrics conditions were faster than password entry . Speaking a PIN was the fastest for biometric sample entry , but short-term memory recall was better in the face verification condition . None of the authentication conditions were considered very usable . Face and voice biometrics conditions were faster than password entry . Speaking a PIN was the fastest for biometric sample entry , but short-term memory recall was better in the face verification condition . None of the authentication conditions were considered very usable . In order to minimize the power consumption , the hardware architecture of a coprocessor dedicated to features extraction is proposed and described in VHDL . This component is designed to be integrated into a System-on-Chip ( SoC ) implementing the complete face verification process , including image acquisition , thereby enabling biometric face authentication to be performed entirely on the mobile device . Comparison of the proposed solution with state-of-the-art academic results and recently disclosed commercial products shows that the chosen approach is indeed much more efficient energy-wise . Part-based methods have seen popular applications for face verification in the wild , since they are more robust to local variations in terms of pose , illumination , and so on . However , most of the part-based approaches are built on hand-crafted features , which may not be suitable for the specific face verification purpose . In this paper , we propose to learn a part-based feature representation under the supervision of face identities through a deep model that ensures that the generated representations are more robust and suitable for face verification . An Artificial neural network was used to classify faces by using obtained features . Phase correlation method was used for face verifying . A MATLAB Graphical user interface was designed by combining these systems for realizing proposed hybrid system , after filtering and scanning methods , the obtained face areas demonstrate within an outline . To correctly classify all the classes simultaneously , the DeepID2 layer must form discriminative identity - related features ( i.e. features with large inter - personal variations ) . The second is face verification signal , which encourages DeepID2 extracted from faces of the same identity to be similar . The verification signal directly regularize DeepID2 and can effectively reduce the intra - personal variations . It models the feature representation of a face as the sum of inter - and intra - personal variations , or , where both and are modeled as Gaussian distributions and are estimated from the training data . Face verification is achieved through log - likelihood ratio test , , where the numerator and denominator are joint probabilities of two faces given the inter - or intra - personal variation hypothesis , respectively . section : Experiments paragraph : Image alignment Image alignment is a key step in many computer vision tasks , including face verification , motion analysis , stereo matching , and object recognition . Alignment results in correspondence across different images by removing intraclass variability and canonicalizing pose . document : Triplet Probabilistic Embedding for Face Verification and Clustering Despite significant progress made over the past twenty five years , unconstrained face verification remains a challenging problem . In this study , we apply the deep convolutional network , a popular deep learning model , to handle this problem . We construct a deep convolutional network called Smile-CNN to perform feature learning and smile detection simultaneously . Experimental results demonstrate that although a deep learning model is generally developed for tackling “ big data , ” the model can also effectively deal with “ small data . Firstly , we pre-trained a CDAE to extract the feature of the first layer , then the extracted feature were used as input of the next basic model CAE , by pre-training the CAE model , we got more abstract feature , then the feature were used to classification . Experiments showed that our approach was useful for smile recognition . On the other hand , we also explored the influence of different number of training samples . Facial expression recognition is one of the most challenging research areas in the image recognition field and has been actively studied since the 70 's . For instance , smile recognition has been studied due to the fact that it is considered an important facial expression in human communication , it is therefore likely useful for human – machine interaction . Moreover , if a smile can be detected and also its intensity estimated , it will raise the possibility of new applications in the future . In this study we focus on the expression of happiness , and propose a physiologically inspired approach for detecting whether a smile is spontaneously produced or not . We use distance-based and angular features that characterize movements of facial regions , and train local classifiers for smile classification . Our experiments with different classifiers on two different datasets show that the eyelid region can be very useful in assessing spontaneous versus posed smiles . To translate the frequency to the base-band , a double-balanced mixer and a voltage-controlled oscillator ( VCO ) are used . The presented results are satisfactory , validating the proposed compact design , and showing the viability of this topology for SMILE applications . The interfaces of these processes are described using WSDL or by an equivalent UML definition . Using the open source AndroMDA tool and starting from the UML interface specification we are able to generate the skeleton of SMILE applications and most part of their business logic . An application developed using SMILE can run on different middleware platforms just changing its binding , i.e. the code that adapts SMILE to a given middleware . Smile detection is a sub-problem of facial expression recognition field , which has attracted more and more interests from researchers because of its wide application market . As for smile detection problem itself , the wild unconstrained scenario is more challenging than the laboratory constrained scenario . Therefore , in this paper , we mainly focus on solving smile detection problem in unconstrained scenarios . Thanks to the introduction of machine learning methods , recent years have seen great progress in the field of automatic facial expression recognition . Commercial real-time expression recognition systems are starting to be used in consumer applications , e.g. , smile detectors embedded in digital cameras [ 62 ] . Nonetheless , considerable progress has yet to be made : Methods for face detection and tracking ( the first step of automated face analysis ) However , the dimension of Gabor features is usually too high to be practically applicable . A method based on Pyramid Histogram of Oriented Gradients(PHOG ) feature and Clustering Linear Discriminate Analysis(CLDA ) is proposed for smile expression recognition . The main merits of the proposed system are that the complexity can be decreased with low-dimension PHOG feature , and the multi-model problem can be overcome by CLDA.The experimental results show that system with PHOG feature achieves competitive or even higher recognition accuracy than with the Gabor feature , but with much lower of computation time cost . Emotion expression recognition is an important aspect for enabling decision making in autonomous agents and systems designed to interact with humans . In this paper , we present our experience in developing a software component for smile intensity detection for multiparty interaction . First , the deep learning architecture and training process is described in detail . We demonstrate a crowd-powered model for the early diagnosis of stroke using a mobile device . The simple approach consists of monitoring the subject 's health in three simple steps including the smile test for facial weakness , raising hands test for arm weakness and speech test for slurring of speech . Our demonstrated system shows a performance accuracy of 87.5 % over a total number of 40 test cases . document : Deep Learning For Smile Recognition Inspired by recent successes of deep learning in computer vision , we propose a novel application of deep convolutional neural networks to facial expression recognition , in particular smile recognition . A smile recognition test accuracy of 99.45 % is achieved for the Denver Intensity of Spontaneous Facial Action In those three cases , accuracies of 65.55 % , 72.94 % and 79.67 % for smile recognition are reported . section : Smile recognition In the following experiments , an aligned version of DISFA is used . We have developed a computer vision system , including both facial feature extraction and recognition , that automatically discriminates among subtly different facial expressions . Expression classification is based on Facial Action Coding System ( FACS ) action units ( AUs ) , and discrimination is performed using Hidden Markov Models ( HMMs ) .
Nowadays face recognition system usage is increasing day by day to provide better security mechanism . But with the face recognition system , there are some spoofing methods also attached using which the system can be befooled easily .
The performance of face recognition systems tends to decrease when images are affected by illumination . Feature extraction is one of the main steps of a face recognition process , where it is possible to alleviate the illumination effects on face images . In order to increase the accuracy of recognition tasks , different methods for obtaining illumination invariant features have been developed .
Furthermore , the PR can be executed parallelly . We applied the PR scheme to a large scale ANN for face image recognition .
Procuring a low-dimensional feature with aggrandized discriminatory power is of pre-eminent importance to face recognition . In this paper a face recognition analysis is suggested based on Hybrid Gaborlet and Kernel Fisher Analysis . Flustered SVD ( Fsvd ) focuses at deriving an illumination invariant image .
First , face discriminant features are obtained from the computation of PCA or interpolation methods as well as Fishers methods . Then , face recognition analysis is carried out by six distance classifiers and their combination based on majority poll and other common rules . Finally , computation results are discussed .
Research of human face recognition is an important topic in the area of pattern recognition and artificial intelligence , and it has very broad application prospects . The extraction of face features is an important part in the process of face automatic recognition . It is essential to introduce the extraction of face features based on integral projection , singular value decomposition and wavelet and so on .
A preliminary study of face-based recognition on a transgender dataset . In this work we investigate a truly novel and extremely unique biometric problem : face-based recognition for transgender persons . A transgender person is someone who under goes a gender transformation via hormone replacement therapy ; that is , a male becomes a female by suppressing natural testosterone production and exogenously increasing estrogen .
It is widely acknowledged that the face identification has played a significant role in the observation system as it does n’t need the object ’s assistance . The definite advantages of face based recognition over other biometrics are distinctiveness and response . As human face is an active object having a high degree of unpredictability in its manifestation , that makes face detection a hard problem in computer vision .
Face Recognition.- Super-Resolved Faces for Improved Face Recognition from Surveillance Video.- Face Detection Based on Multi-Block LBP Representation.- Color Face Tensor Factorization and Slicing for Illumination-Robust Recognition.- Robust Real-Time Face Detection Using Face Certainty Map.- Poster I.- Motion Compensation for Face Recognition Based on Active Differential Imaging.- Face Recognition with Local Gabor Textons.- Speaker Verification with Adaptive Spectral Subband Centroids.- Similarity Rank Correlation for Face Recognition Under Unenrolled Pose.- Feature Correlation Filter for Face Recognition.- Face Recognition by Discriminant Analysis with Gabor Tensor Representation.- Fingerprint Enhancement Based on Discrete Cosine Transform.- Biometric Template Classification : A Case Study in Iris Textures.- Protecting Biometric Templates with Image Watermarking Techniques.- Factorial Hidden Markov Models for Gait Recognition.- A Robust Fingerprint Matching Approach : Growing and Fusing of Local Structures.- Automatic Facial Pose Determination of 3D Range Data for Face Model and Expression Identification.- SVDD-Based Illumination Compensation for Face Recognition.- Keypoint Identification and Feature-Based 3D Face Recognition.- Fusion of Near Infrared Face and Iris Biometrics.- Multi-Eigenspace Learning for Video-Based Face Recognition.- Error-Rate Based Biometrics
Oral - I.- Learning Personal Specific Facial Dynamics for Face Recognition from Videos.- A New Probabilistic Model for Recognizing Signs with Systematic Modulations.- Model-Based Stereo with Occlusions.- View Invariant Head Recognition by Hybrid PCA Based Reconstruction.- Poster - I.- Person-Independent Monocular Tracking of Face and Facial Actions with Multilinear Models.- Automatic Facial Expression Recognition Using Boosted Discriminatory Classifiers.- Generating Body Surface Deformation Using Level Set Method.- Patch-Based Pose Inference with a Mixture of Density Estimators.- Integrating Multiple Visual Cues for Robust Real-Time 3D Face Tracking.- Model-Assisted 3D Face Reconstruction from Video.- Human Perambulation as a Self Calibrating Biometric.- Oral - II.- Detecting , Localizing and Classifying Visual Traits from Arbitrary Viewpoints Using Probabilistic Local Feature Modeling.- Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions.- Structured Ordinal Features for Appearance-Based Object Representation.- SODA-Boosting and Its Application to Gender Recognition.- Poster - II.- Single Image Subspace for Face Recognition.- Human Face Processing with 1.5D Models.- Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition.- A Unified Framework of Subspace and Distance Metric Learning for Face Recognition.- Face Recognition Based on Pose-Variant Image Synthesis and Multi-level Multi-feature Fusion.- Towards Pose-Invariant 2D Face Classification for Surveillance.- Robust Face Recognition Strategies Using Feed-Forward Architectures and Parts .
We introduce a fast , robust , accurate eye localization algorithm . Detecting and normalizing human faces from live video streams is the first crucial step in a face verification/recognition system . The accuracy and robustness affect the performance of the following face registration and classification .
We perform thorough analysis of various probability calibration techniques with the help of methods inspired from meteorology . We test our methods on Labeled Faces in the Wild dataset and show suitability of the proposed approach for face verification/recognition .
Face restoration from low resolution and noise is important for applications of face analysis recognition . However , most existing face restoration models omit the multiple scale issues in the face restoration problem , which is still not well solved in the research area .
Single chip solutions with focus on reconfiguration , low area , and high speed are desirable for such applications . This thesis reports hardware-directed algorithms and array-type Very Large Scale Integrated ( VLSI ) architectures for navigation and face-based human recognition . The key contributions are listed below .
Transfer learning using supervised features has been successful in several computer vision applications . Striking examples include face recognition and visual question answering , where image features trained on ImageNet and word embeddings trained on large unsupervised corpora are combined . In contrast , most approaches for sentence representation learning are unsupervised , arguably because the NLP community has not yet found the best supervised task for embedding the semantics of a whole sentence .
Convolutional neural networks ( CNN ) date back decades [ reference ] and deep CNNs have recently shown an explosive popularity partially due to its success in image classification [ reference ] , [ reference ] . They have also been successfully applied to other computer vision fields , such as object detection [ reference ] , [ reference ] , [ reference ] , face recognition [ reference ] , and pedestrian detection [ reference ] . Several factors are of central importance in this progress : ( i ) the efficient training implementation on modern powerful GPUs [ reference ] , ( ii ) the proposal of the Rectified Linear Unit ( ReLU ) [ reference ] which makes convergence much faster while still presents good quality [ reference ] , and ( iii ) the easy access to an abundance of data ( like ImageNet [ reference ] ) for training larger models .
2D Face Alignment . Face alignment is a critical step for any facial analysis task such as face recognition . With enhancement in the modeling , we hope to improve this task ( Fig .
First , there are often one or two valid scales in the image , and the scale - forecast network can automatically select some particular scales , and ignore all the other invalid ones in the multi - scale test stage ; second , the input of LRN descends from the output of RSA to predict feature maps at smaller scales ; it is not necessary to compute feature maps of multiple scales in a multi - shot manner as RPN _ m does . subsection : Face Detection Fig .
[ reference ] illustrates this augmentation process . Specifically , following face detection and landmark detection , we transform detected bounding boxes and their detected facial landmarks using a number of simple in - plane transformations . The parameters for these transformations are selected randomly from fixed distributions ( Table .
By classifying face images into thousands or even millions of identities , the last hidden layer forms features highly discriminative to identities . The second is supervising deep neural networks with both face identification and verification tasks . The verification task minimizes the distance between features of the same identity , and decreases intra - personal variations .
These representations have shown strong performance for recognition of imagery and video in - the - wild in unconstrained datasets , with recent approaches demonstrating capabilities that exceed human performance on the well known Labeled Faces in the Wild dataset . The problem of face recognition may be described in terms of face verification and face identification . Face verification involves computing a one - to - one similarity between a probe image and a reference image , to determine if two image observations are of the same subject .
document : Orthogonal Deep Features Decomposition for Age - Invariant Face Recognition As facial appearance is subject to significant intra - class variations caused by the aging process over time , age - invariant face recognition ( AIFR ) remains a major challenge in face recognition community . To reduce the intra - class discrepancy caused by the aging , in this paper we propose a novel approach ( namely , Orthogonal Embedding CNNs , or OE - CNNs ) to learn the age - invariant deep face features .
This way , the aggregation module can handle an arbitrary set of image or video faces without temporal information ( e.g. that collected from different Internet locations ) . Third , the module should be adaptive to the input faces and has parameters trainable through supervised learning in a standard face recognition training task . Our solution is inspired by the memory attention mechanism described in [ reference ][ reference ][ reference ] .
[ reference ] ( a ) ) . Lastly , in comparison to human face verification , car verification , which targets at verifying whether two cars belong to the same model , is an interesting and under - researched problem . The unconstrained viewpoints make car verification arguably more challenging than traditional face verification .
When lighting condition changes , the appearance of a face varies intensely . In this paper , an illumination normalization method for face verification under various lighting conditions is presented . In the proposed approach , 2D wavelet is applied on the Yale database B and XM2VTS human face databases for illumination normalization in face verification .
In recent , face verification technique is applied to mobile devices to authenticate owner of them . Face verification on mobile devices needs to be considered with both performance and computational cost which is trade-off . In this paper , we propose lightweight network architectures and fusion methods for efficient face verification task on mobile devices .
The ELM advocates the convergence of machine learning and biological learning for pervasive learning and intelligence and has been extensively researched in widespread applications . Nonetheless , till date , none of the work of ELM has proved its competency in tackling face verification problem . Hence , in this paper , we are going to probe for the first time the feasibility of ELM-based network in handling the face verification task .
In the preliminary analysis , feature vectors extracted from different views of the same Lytro picture are proved different enough to provide complementary information beneficial for face recognition purpose . Starting from a set of multiple views for each data , face verification problem is tackled and results are compared with those achieved with classical 2D images simulated using a single view , i.e. the central one . Two experiments are described and , in both cases , the presented method shows superior performances than standard algorithms adopted by classical imaging sensors .
Priority is given to regions which signal high intensity variance , thereby allowing the most probable eye regions to be selected . On detecting a mouth and two eyes , a face verification step based on Eigenface theory is applied to a normalized search space in the image relative to the distance between the eye feature points .
Facial features such as eyes , nose and mouth are automatically detected based on properties of the associated image regions . On detecting a mouth , a nose and two eyes , a face verification step based on Eigen face theory is applied to a normalized search space in the image relative to the distance between the eye feature points . The experiments were carried out on test images taken from the internet and various other randomly selected sources .
The underlying assumption in identification is that the gallery always has sufficient samples per subject to linearly reconstruct a query image . Unfortunately , such assumption is easily violated in the more challenging and realistic face verification scenario . A verification algorithm is required to determine if two faces ( where one or both have not been seen before ) belong to the same person , while explicitly taking into account the possibility of impostor attacks .
The underlying assumption in SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class . Unfortunately , such assumption is easily violated in the more challenging face verification scenario , where an algorithm is required to determine if two faces ( where one or both have not been seen before ) belong to the same person . In this paper , we first discuss why previous attempts with SR might not be applicable to verification problems .
Video-replay spoofing is a serious threat for face authentication/verification system . A real-time approach to detect video-replay attack using a generic webcamera is presented .
Face and voice biometrics conditions were faster than password entry . Speaking a PIN was the fastest for biometric sample entry , but short-term memory recall was better in the face verification condition . None of the authentication conditions were considered very usable .
Face and voice biometrics conditions were faster than password entry . Speaking a PIN was the fastest for biometric sample entry , but short-term memory recall was better in the face verification condition . None of the authentication conditions were considered very usable .
In order to minimize the power consumption , the hardware architecture of a coprocessor dedicated to features extraction is proposed and described in VHDL . This component is designed to be integrated into a System-on-Chip ( SoC ) implementing the complete face verification process , including image acquisition , thereby enabling biometric face authentication to be performed entirely on the mobile device . Comparison of the proposed solution with state-of-the-art academic results and recently disclosed commercial products shows that the chosen approach is indeed much more efficient energy-wise .
Part-based methods have seen popular applications for face verification in the wild , since they are more robust to local variations in terms of pose , illumination , and so on . However , most of the part-based approaches are built on hand-crafted features , which may not be suitable for the specific face verification purpose . In this paper , we propose to learn a part-based feature representation under the supervision of face identities through a deep model that ensures that the generated representations are more robust and suitable for face verification .
An Artificial neural network was used to classify faces by using obtained features . Phase correlation method was used for face verifying . A MATLAB Graphical user interface was designed by combining these systems for realizing proposed hybrid system , after filtering and scanning methods , the obtained face areas demonstrate within an outline .
To correctly classify all the classes simultaneously , the DeepID2 layer must form discriminative identity - related features ( i.e. features with large inter - personal variations ) . The second is face verification signal , which encourages DeepID2 extracted from faces of the same identity to be similar . The verification signal directly regularize DeepID2 and can effectively reduce the intra - personal variations .
It models the feature representation of a face as the sum of inter - and intra - personal variations , or , where both and are modeled as Gaussian distributions and are estimated from the training data . Face verification is achieved through log - likelihood ratio test , , where the numerator and denominator are joint probabilities of two faces given the inter - or intra - personal variation hypothesis , respectively . section : Experiments
paragraph : Image alignment Image alignment is a key step in many computer vision tasks , including face verification , motion analysis , stereo matching , and object recognition . Alignment results in correspondence across different images by removing intraclass variability and canonicalizing pose .
document : Triplet Probabilistic Embedding for Face Verification and Clustering Despite significant progress made over the past twenty five years , unconstrained face verification remains a challenging problem .
In this study , we apply the deep convolutional network , a popular deep learning model , to handle this problem . We construct a deep convolutional network called Smile-CNN to perform feature learning and smile detection simultaneously . Experimental results demonstrate that although a deep learning model is generally developed for tackling “ big data , ” the model can also effectively deal with “ small data .
Firstly , we pre-trained a CDAE to extract the feature of the first layer , then the extracted feature were used as input of the next basic model CAE , by pre-training the CAE model , we got more abstract feature , then the feature were used to classification . Experiments showed that our approach was useful for smile recognition . On the other hand , we also explored the influence of different number of training samples .
Facial expression recognition is one of the most challenging research areas in the image recognition field and has been actively studied since the 70 's . For instance , smile recognition has been studied due to the fact that it is considered an important facial expression in human communication , it is therefore likely useful for human – machine interaction . Moreover , if a smile can be detected and also its intensity estimated , it will raise the possibility of new applications in the future .
In this study we focus on the expression of happiness , and propose a physiologically inspired approach for detecting whether a smile is spontaneously produced or not . We use distance-based and angular features that characterize movements of facial regions , and train local classifiers for smile classification . Our experiments with different classifiers on two different datasets show that the eyelid region can be very useful in assessing spontaneous versus posed smiles .
To translate the frequency to the base-band , a double-balanced mixer and a voltage-controlled oscillator ( VCO ) are used . The presented results are satisfactory , validating the proposed compact design , and showing the viability of this topology for SMILE applications .
The interfaces of these processes are described using WSDL or by an equivalent UML definition . Using the open source AndroMDA tool and starting from the UML interface specification we are able to generate the skeleton of SMILE applications and most part of their business logic . An application developed using SMILE can run on different middleware platforms just changing its binding , i.e. the code that adapts SMILE to a given middleware .
Smile detection is a sub-problem of facial expression recognition field , which has attracted more and more interests from researchers because of its wide application market . As for smile detection problem itself , the wild unconstrained scenario is more challenging than the laboratory constrained scenario . Therefore , in this paper , we mainly focus on solving smile detection problem in unconstrained scenarios .
Thanks to the introduction of machine learning methods , recent years have seen great progress in the field of automatic facial expression recognition . Commercial real-time expression recognition systems are starting to be used in consumer applications , e.g. , smile detectors embedded in digital cameras [ 62 ] . Nonetheless , considerable progress has yet to be made : Methods for face detection and tracking ( the first step of automated face analysis )
However , the dimension of Gabor features is usually too high to be practically applicable . A method based on Pyramid Histogram of Oriented Gradients(PHOG ) feature and Clustering Linear Discriminate Analysis(CLDA ) is proposed for smile expression recognition . The main merits of the proposed system are that the complexity can be decreased with low-dimension PHOG feature , and the multi-model problem can be overcome by CLDA.The experimental results show that system with PHOG feature achieves competitive or even higher recognition accuracy than with the Gabor feature , but with much lower of computation time cost .
Emotion expression recognition is an important aspect for enabling decision making in autonomous agents and systems designed to interact with humans . In this paper , we present our experience in developing a software component for smile intensity detection for multiparty interaction . First , the deep learning architecture and training process is described in detail .
We demonstrate a crowd-powered model for the early diagnosis of stroke using a mobile device . The simple approach consists of monitoring the subject 's health in three simple steps including the smile test for facial weakness , raising hands test for arm weakness and speech test for slurring of speech . Our demonstrated system shows a performance accuracy of 87.5 % over a total number of 40 test cases .
document : Deep Learning For Smile Recognition Inspired by recent successes of deep learning in computer vision , we propose a novel application of deep convolutional neural networks to facial expression recognition , in particular smile recognition . A smile recognition test accuracy of 99.45 % is achieved for the Denver Intensity of Spontaneous Facial Action
In those three cases , accuracies of 65.55 % , 72.94 % and 79.67 % for smile recognition are reported . section : Smile recognition In the following experiments , an aligned version of DISFA is used .
We have developed a computer vision system , including both facial feature extraction and recognition , that automatically discriminates among subtly different facial expressions . Expression classification is based on Facial Action Coding System ( FACS ) action units ( AUs ) , and discrimination is performed using Hidden Markov Models ( HMMs ) .
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