Face alignment is an essential task for facial performance capture and expression analysis . As a complex nonlinear problem in computer vision , face alignment across poses is still not studied well . Although the state-of-the-art Supervised Descent Method ( SDM ) has shown good performance , it learns conflict descent direction in the whole complex space due to various poses and expressions . Keypoint detection is one of the most importantpre-processing steps in tasks such as face modeling , recognitionand verification . In this paper , we present an iterative methodfor Keypoint Estimation and Pose prediction of unconstrainedfaces by Learning Efficient H-CNN Regressors ( KEPLER ) foraddressing the face alignment problem . Recent state of the artmethods have shown improvements in face keypoint detectionby employing Convolution Neural Networks ( CNNs ) . We solve the face alignment problem by two-stage processes . The first stage is response mapping stage , we use deep Purely Convolutional Network ( a specialised Convolutional Neural Network designed for face alignment problem ) to reconstruct the response maps . The second stage is shape mapping stage , which processes the response maps to get locations of facial key points . In this thesis , we study the problem of discovering degenerate structures of high-dimensional inputs using these techniques . Especially we focus ourselves on low-dimensional subspaces and their unions , and address their application in overcoming the challenges encountered under three practical scenarios : face image alignment , background subtraction and automatic plant identification . In facial image alignment , we propose a method that jointly brings multiple images of an unseen face into alignment with a pre-trained generic appearance model despite different poses , expressions and illumination conditions of the face in the images . This is followed by classification of aligned faces . In literature , face alignment process is usually done with manually and high recognition rates can be achieved due to very well aligned faces . However , in real-time face recognition applications , it 's not possible to align face images manually . The latter is extracted from normalized local regions by Canny edge filtering and is robust at coarse alignment in spite of poor initialization . The different merits of these two forms of intrinsic information motivate us to employ them at different stages of our face alignment process . Extensive experimentations show that this proposed approach allows our system to handle not only illumination variation , but also poor initialization . A novel method of face gender classifier construction is proposed and evaluated . Previously , researchers have assumed that a computationally expensive face alignment step ( in which the face image is transformed so that facial landmarks such as the eyes , nose , chin , etc , are in uniform locations in the image ) is required in order to maximize the accuracy of predictions on new face images . We , however , argue that this step is not necessary , and that machine learning classifiers can be made robust to face misalignments by automatically expanding the training data with examples of faces that have been deliberately misaligned ( for example , translated or rotated ) . In our algorithm pipeline , the face detection step eliminates slow floating point computations using pixel intensity binary test , and reduce search scope effectively . In the face alignment step , we utilize the high performance of local binary feature and extend the single pose regression model to handle large rotations . The pose estimation step uses a mean rigid face model to calculate head pose fast by solving 2D-3D correspondence . We then develop a rule-based multimodal framework to quantitatively evaluate each child . Our system involves speech recognition based automatic name calling detection , face detection/alignment , head pose estimation , and considers the response speed , eye contact duration and head orientation to output the final prediction . Compared to existing work , our dataset characterizes a more precise and detailed scoring system with clinical trial standards , as well as a more spontaneous setting by incorporating less lab-controlled sessions with dynamic/cluttered environments , multi-pose mobile captured videos , and flexible number of accompanying adults . We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method , reducing the error up to 16 % relatively . To facilitate practical usage of face detection/alignment methods , we also propose a convenient metric to measure how good a detector is for alignment initialization . This paper provides an in-depth analysis on face shape alignment for pose insensitive face recognition . The dissimilarity between two face images can be modeled as the difference in intensity between these two images , obtained by warping these faces onto the same shape . For example , the user can drag a mouth corner to find faces with wider smiles , or the tip of the nose to find faces with a specific pose . As part of our system , we propose ( 1 ) a novel confidence score for face alignment results that automatically constructs a contour-aligned face database with reasonable alignment accuracy , ( 2 ) a simple and straightforward extension of PCA with missing data to tensor analysis , and ( 3 ) a new regularized tensor model to compute shape feature vectors for each aligned face , all built upon previous work . To the best of our knowledge , our system demonstrates the first face retrieval approach based chiefly on shape manipulation . We describe an automatic method for beautifying digital portraits by smoothing the skin of the face . The method builds on existing face detection and face feature alignment technology to automatically segment the face and neck areas to be smoothed . A smoothing filter is then applied to these areas . In contrast to aforementioned works that use a fixed 3DMM model , our model and model fitting are learned jointly . This results in a more powerful model : a single - pass encoder , which is learnt jointly with the model , achieves state - of - the - art face alignment performance on AFLW2000 benchmark dataset . 3D Face Reconstruction . 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 . section : INTRODUCTION In the past few years , random forest ( RF ) [ reference ][ reference ] as a machine - learning tool , working via an ensemble of multiple decision trees , has been employed for efficient classification or regression problems , and applied to a large variety of computer - vision applications , such as object recognition [ reference ] , face alignment [ reference ][ reference ][ reference ] , data clustering [ reference ] , single image super - resolution ( SISR ) [ reference ][ reference ] , and so on . As seen in Figure 1 ( c ) , it is very difficult to ask a single regressor to perform perfectly uniformly at all quality levels . The difficult regression task can be decomposed into a sequence of simpler steps , inspired by the works of cascade pose regression [ reference ] and face alignment [ reference ][ reference ] . Instead of using cascaded networks , Honari et al . proposed Recombinator Networks ( RecNet ) for learning coarse - to - fine feature aggregation with multi - scale input maps , where each branch extracts features based on current maps and the feature maps of coarser branches . A few methods employ a single network to solve the face alignment problem . Shao et al . proposed a Coarse - to - Fine Training ( CFT ) method to learn the mapping from input face patches to estimated shapes , which searches the solutions smoothly by adjusting the relative weights between principal landmarks and elaborate landmarks . Other visual localization tasks , like face detection , usually use multiple cascaded shallow networks rather than a single very deep network . Finally , common face alignment benchmarks only contain thousands of training images . A simple network is not easy to overfit given a small amount of raw training data . is the set of parameters including weights and biases of our network MCL , which is updated using Mini - Batch Stochastic Gradient Descent ( SGD ) at each iteration . The face alignment loss is defined as where is the weight of the - th landmark , and denote the ground - truth x - coordinate and y - coordinate of the - th landmark respectively , and is the ground truth inter - ocular distance between the eye centers . Inter - ocular distance normalization provides fair comparisons among faces with different size , and reduces the magnitude of loss to speed up the learning process . The system finds the individuals of interest from the videos in real-time at 18 fps . This research also introduces the Celebrities Video Aging ( CVA ) dataset used for evaluating the deep network which hopefully may encourage researchers interested in using deep learning for age-invariant face recognition . Hence there are many challenges still open in compensating age related variations . In this paper we have proposed a novel self-PCA based approach in order to consider distinctiveness of the effects of aging of a person for age invariant face recognition . The region around the eyes is used as the input feature instead of the entire face as it is more stable part of the face with respect to aging and also requires less space . In order to overcome such drawbacks , we present in this paper the first , to the best of knowledge , manually collected " in-the-wild " age database , dubbed AgeDB , containing images annotated with accurate to the year , noise-free labels . As demonstrated by a series of experiments utilizing state-of-the-art algorithms , this unique property renders AgeDB suitable when performing experiments on age-invariant face verification , age estimation and face age progression " in-the-wild " . In particular , we introduce several training strategies to reduce the computational cost and overcome insufficient memory capacity . We evaluate our method on three tasks : age-invariant face identification on the MORPH database , age-invariant face retrieval on the CACD database and age-invariant face verification on CACD-VS database . The experimental results demonstrate the effectiveness of our approach . This chapter presents methods for audio- , visual- and for the integrated audio and visual information processing using brain-inspired SNN architectures such as NeuCube . Case studies are presented for short musical pieces recognition , fast moving object recognition , age-invariant face identification , moving digits recognition and other . Such kind of method has robustness to changes of illumination , posture , expression , shape and texture by combining Gabor transform , LBP and convolutional neural network . Experiments are implemented on the FG-NET database and the results can outperform the state of the art ones , which verify the validity of the proposed method in age invariant face recognition problem . In this paper , some existing face recognition approaches , in terms of robustness to aging , have been reviewed briefly . Also , the experimental results from these methods have been compared using the common databases in age-invariant face recognition applications . The comparison results indicate that the approaches which consider both component-based representation of facial images and identity factors outperform the other existing methods . Third , we develop effective and novel training strategies for end-to-end learning the whole deep architecture , which generates powerful age-invariant face representations explicitly disentangled from the age variation . Moreover , we propose a new large-scale Cross-Age Face Recognition ( CAFR ) benchmark dataset to facilitate existing efforts and push the frontiers of age-invariant face recognition research . Extensive experiments on both our CAFR and several other cross-age datasets ( MORPH , CACD and FG-NET ) demonstrate the superiority of the proposed AIM model over the state-of-the-arts . Longitudinal analysis shows that despite decreasing genuine scores , 99 % of subjects can still be recognized at 0.01 % FAR up to approximately 6 years elapsed time , and that age , sex , and race only marginally influence these trends . The methodology presented here should be periodically repeated to determine age-invariant properties of face recognition as state-of-the-art evolves to better address facial aging . Per component performance analysis shows that the nose is the most stable component during face aging . Age invariant recognition exploiting demographics shows that face aging has more influence on females than males . Overall , recognition performance on the two databases shows that the proposed component based approach is more robust to large time lapses than FaceVACS , a leading commercial face matcher . 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 . Principal Component Analysis ( PCA ) is a statistical technique used for dimension reduction and recognition , & widely used for facial feature extraction and recognition . In this paper a cluster based SPCA face recognition method has been proposed . The accuracy of the facial feature location and extraction has largely influenced the results of final recognition . The paper has cleared the importance of facial feature extraction and recognition , then has applied the curvelet transform to the process of facial feature extraction and recognition . The paper has studied the implementation pre-process of facial feature extraction , and further analyzed the interception processing , processing methods and slicing based on the curvelet transform . Hence , the problem of computer-based face recognition using impartial facial data as analysis is still largely an unexplored area of research . For the fact that the way humans carry out facial recognition and verification is different from how machines do , it must be fascinating to see how a machine favors different pieces of the face when exhibited to the difficulties of face recognition . This paper , therefore , investigates the question that encompasses the idea of face recognition using incomplete facial information . 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 Efficient accomplishment of Face Recognition confronts innumerable hurdles in the form of variations in lighting conditions during image capture , Occlusions , damage in facial portions due to accidents etc . The application of Facial Image Inpainting also fails when the occlusions or the deformalities are present across the boundary of the object of interest(face ) , since the bounds for the application of the inpainting algorithm is not precisely defined . Hence recovery of the complete picture of a human face from partially occluded images is quite a challenge in Image Processing . A novel approach is proposed for facial inpainting based on a guidance face . The guidance face matches with the high-level facial attributes of the target face . A novel approach is proposed for facial inpainting based on a guidance face . The guidance face matches with the high-level facial attributes of the target face . Artists and fashion designers have recently been creating a new form of art -- Camouflage Art -- which can be used to prevent computer vision algorithms from detecting faces . This digital art technique combines makeup and hair styling , or other modifications such as facial painting to help avoid automatic face-detection . In this paper , we first study the camouflage interference and its effectiveness on several current state of art techniques in face detection/recognition ; and then present a tool that can facilitate digital art design for such camouflage that can fool these computer vision algorithms .
Face alignment is an essential task for facial performance capture and expression analysis . As a complex nonlinear problem in computer vision , face alignment across poses is still not studied well . Although the state-of-the-art Supervised Descent Method ( SDM ) has shown good performance , it learns conflict descent direction in the whole complex space due to various poses and expressions .
Keypoint detection is one of the most importantpre-processing steps in tasks such as face modeling , recognitionand verification . In this paper , we present an iterative methodfor Keypoint Estimation and Pose prediction of unconstrainedfaces by Learning Efficient H-CNN Regressors ( KEPLER ) foraddressing the face alignment problem . Recent state of the artmethods have shown improvements in face keypoint detectionby employing Convolution Neural Networks ( CNNs ) .
We solve the face alignment problem by two-stage processes . The first stage is response mapping stage , we use deep Purely Convolutional Network ( a specialised Convolutional Neural Network designed for face alignment problem ) to reconstruct the response maps . The second stage is shape mapping stage , which processes the response maps to get locations of facial key points .
In this thesis , we study the problem of discovering degenerate structures of high-dimensional inputs using these techniques . Especially we focus ourselves on low-dimensional subspaces and their unions , and address their application in overcoming the challenges encountered under three practical scenarios : face image alignment , background subtraction and automatic plant identification . In facial image alignment , we propose a method that jointly brings multiple images of an unseen face into alignment with a pre-trained generic appearance model despite different poses , expressions and illumination conditions of the face in the images .
This is followed by classification of aligned faces . In literature , face alignment process is usually done with manually and high recognition rates can be achieved due to very well aligned faces . However , in real-time face recognition applications , it 's not possible to align face images manually .
The latter is extracted from normalized local regions by Canny edge filtering and is robust at coarse alignment in spite of poor initialization . The different merits of these two forms of intrinsic information motivate us to employ them at different stages of our face alignment process . Extensive experimentations show that this proposed approach allows our system to handle not only illumination variation , but also poor initialization .
A novel method of face gender classifier construction is proposed and evaluated . Previously , researchers have assumed that a computationally expensive face alignment step ( in which the face image is transformed so that facial landmarks such as the eyes , nose , chin , etc , are in uniform locations in the image ) is required in order to maximize the accuracy of predictions on new face images . We , however , argue that this step is not necessary , and that machine learning classifiers can be made robust to face misalignments by automatically expanding the training data with examples of faces that have been deliberately misaligned ( for example , translated or rotated ) .
In our algorithm pipeline , the face detection step eliminates slow floating point computations using pixel intensity binary test , and reduce search scope effectively . In the face alignment step , we utilize the high performance of local binary feature and extend the single pose regression model to handle large rotations . The pose estimation step uses a mean rigid face model to calculate head pose fast by solving 2D-3D correspondence .
We then develop a rule-based multimodal framework to quantitatively evaluate each child . Our system involves speech recognition based automatic name calling detection , face detection/alignment , head pose estimation , and considers the response speed , eye contact duration and head orientation to output the final prediction . Compared to existing work , our dataset characterizes a more precise and detailed scoring system with clinical trial standards , as well as a more spontaneous setting by incorporating less lab-controlled sessions with dynamic/cluttered environments , multi-pose mobile captured videos , and flexible number of accompanying adults .
We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method , reducing the error up to 16 % relatively . To facilitate practical usage of face detection/alignment methods , we also propose a convenient metric to measure how good a detector is for alignment initialization .
This paper provides an in-depth analysis on face shape alignment for pose insensitive face recognition . The dissimilarity between two face images can be modeled as the difference in intensity between these two images , obtained by warping these faces onto the same shape .
For example , the user can drag a mouth corner to find faces with wider smiles , or the tip of the nose to find faces with a specific pose . As part of our system , we propose ( 1 ) a novel confidence score for face alignment results that automatically constructs a contour-aligned face database with reasonable alignment accuracy , ( 2 ) a simple and straightforward extension of PCA with missing data to tensor analysis , and ( 3 ) a new regularized tensor model to compute shape feature vectors for each aligned face , all built upon previous work . To the best of our knowledge , our system demonstrates the first face retrieval approach based chiefly on shape manipulation .
We describe an automatic method for beautifying digital portraits by smoothing the skin of the face . The method builds on existing face detection and face feature alignment technology to automatically segment the face and neck areas to be smoothed . A smoothing filter is then applied to these areas .
In contrast to aforementioned works that use a fixed 3DMM model , our model and model fitting are learned jointly . This results in a more powerful model : a single - pass encoder , which is learnt jointly with the model , achieves state - of - the - art face alignment performance on AFLW2000 benchmark dataset . 3D Face Reconstruction .
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 .
section : INTRODUCTION In the past few years , random forest ( RF ) [ reference ][ reference ] as a machine - learning tool , working via an ensemble of multiple decision trees , has been employed for efficient classification or regression problems , and applied to a large variety of computer - vision applications , such as object recognition [ reference ] , face alignment [ reference ][ reference ][ reference ] , data clustering [ reference ] , single image super - resolution ( SISR ) [ reference ][ reference ] , and so on .
As seen in Figure 1 ( c ) , it is very difficult to ask a single regressor to perform perfectly uniformly at all quality levels . The difficult regression task can be decomposed into a sequence of simpler steps , inspired by the works of cascade pose regression [ reference ] and face alignment [ reference ][ reference ] .
Instead of using cascaded networks , Honari et al . proposed Recombinator Networks ( RecNet ) for learning coarse - to - fine feature aggregation with multi - scale input maps , where each branch extracts features based on current maps and the feature maps of coarser branches . A few methods employ a single network to solve the face alignment problem . Shao et al . proposed a Coarse - to - Fine Training ( CFT ) method to learn the mapping from input face patches to estimated shapes , which searches the solutions smoothly by adjusting the relative weights between principal landmarks and elaborate landmarks .
Other visual localization tasks , like face detection , usually use multiple cascaded shallow networks rather than a single very deep network . Finally , common face alignment benchmarks only contain thousands of training images . A simple network is not easy to overfit given a small amount of raw training data .
is the set of parameters including weights and biases of our network MCL , which is updated using Mini - Batch Stochastic Gradient Descent ( SGD ) at each iteration . The face alignment loss is defined as where is the weight of the - th landmark , and denote the ground - truth x - coordinate and y - coordinate of the - th landmark respectively , and is the ground truth inter - ocular distance between the eye centers . Inter - ocular distance normalization provides fair comparisons among faces with different size , and reduces the magnitude of loss to speed up the learning process .
The system finds the individuals of interest from the videos in real-time at 18 fps . This research also introduces the Celebrities Video Aging ( CVA ) dataset used for evaluating the deep network which hopefully may encourage researchers interested in using deep learning for age-invariant face recognition .
Hence there are many challenges still open in compensating age related variations . In this paper we have proposed a novel self-PCA based approach in order to consider distinctiveness of the effects of aging of a person for age invariant face recognition . The region around the eyes is used as the input feature instead of the entire face as it is more stable part of the face with respect to aging and also requires less space .
In order to overcome such drawbacks , we present in this paper the first , to the best of knowledge , manually collected " in-the-wild " age database , dubbed AgeDB , containing images annotated with accurate to the year , noise-free labels . As demonstrated by a series of experiments utilizing state-of-the-art algorithms , this unique property renders AgeDB suitable when performing experiments on age-invariant face verification , age estimation and face age progression " in-the-wild " .
In particular , we introduce several training strategies to reduce the computational cost and overcome insufficient memory capacity . We evaluate our method on three tasks : age-invariant face identification on the MORPH database , age-invariant face retrieval on the CACD database and age-invariant face verification on CACD-VS database . The experimental results demonstrate the effectiveness of our approach .
This chapter presents methods for audio- , visual- and for the integrated audio and visual information processing using brain-inspired SNN architectures such as NeuCube . Case studies are presented for short musical pieces recognition , fast moving object recognition , age-invariant face identification , moving digits recognition and other .
Such kind of method has robustness to changes of illumination , posture , expression , shape and texture by combining Gabor transform , LBP and convolutional neural network . Experiments are implemented on the FG-NET database and the results can outperform the state of the art ones , which verify the validity of the proposed method in age invariant face recognition problem .
In this paper , some existing face recognition approaches , in terms of robustness to aging , have been reviewed briefly . Also , the experimental results from these methods have been compared using the common databases in age-invariant face recognition applications . The comparison results indicate that the approaches which consider both component-based representation of facial images and identity factors outperform the other existing methods .
Third , we develop effective and novel training strategies for end-to-end learning the whole deep architecture , which generates powerful age-invariant face representations explicitly disentangled from the age variation . Moreover , we propose a new large-scale Cross-Age Face Recognition ( CAFR ) benchmark dataset to facilitate existing efforts and push the frontiers of age-invariant face recognition research . Extensive experiments on both our CAFR and several other cross-age datasets ( MORPH , CACD and FG-NET ) demonstrate the superiority of the proposed AIM model over the state-of-the-arts .
Longitudinal analysis shows that despite decreasing genuine scores , 99 % of subjects can still be recognized at 0.01 % FAR up to approximately 6 years elapsed time , and that age , sex , and race only marginally influence these trends . The methodology presented here should be periodically repeated to determine age-invariant properties of face recognition as state-of-the-art evolves to better address facial aging .
Per component performance analysis shows that the nose is the most stable component during face aging . Age invariant recognition exploiting demographics shows that face aging has more influence on females than males . Overall , recognition performance on the two databases shows that the proposed component based approach is more robust to large time lapses than FaceVACS , a leading commercial face matcher .
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 .
Principal Component Analysis ( PCA ) is a statistical technique used for dimension reduction and recognition , & widely used for facial feature extraction and recognition . In this paper a cluster based SPCA face recognition method has been proposed .
The accuracy of the facial feature location and extraction has largely influenced the results of final recognition . The paper has cleared the importance of facial feature extraction and recognition , then has applied the curvelet transform to the process of facial feature extraction and recognition . The paper has studied the implementation pre-process of facial feature extraction , and further analyzed the interception processing , processing methods and slicing based on the curvelet transform .
Hence , the problem of computer-based face recognition using impartial facial data as analysis is still largely an unexplored area of research . For the fact that the way humans carry out facial recognition and verification is different from how machines do , it must be fascinating to see how a machine favors different pieces of the face when exhibited to the difficulties of face recognition . This paper , therefore , investigates the question that encompasses the idea of face recognition using incomplete facial information .
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
Efficient accomplishment of Face Recognition confronts innumerable hurdles in the form of variations in lighting conditions during image capture , Occlusions , damage in facial portions due to accidents etc . The application of Facial Image Inpainting also fails when the occlusions or the deformalities are present across the boundary of the object of interest(face ) , since the bounds for the application of the inpainting algorithm is not precisely defined . Hence recovery of the complete picture of a human face from partially occluded images is quite a challenge in Image Processing .
A novel approach is proposed for facial inpainting based on a guidance face . The guidance face matches with the high-level facial attributes of the target face .
A novel approach is proposed for facial inpainting based on a guidance face . The guidance face matches with the high-level facial attributes of the target face .
Artists and fashion designers have recently been creating a new form of art -- Camouflage Art -- which can be used to prevent computer vision algorithms from detecting faces . This digital art technique combines makeup and hair styling , or other modifications such as facial painting to help avoid automatic face-detection . In this paper , we first study the camouflage interference and its effectiveness on several current state of art techniques in face detection/recognition ; and then present a tool that can facilitate digital art design for such camouflage that can fool these computer vision algorithms .
Sign-Correlation Partition Based on Global Supervised Descent Method for Face Alignment KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors Learning Facial Point Response for Alignment by Purely Convolutional Network Calculating degenerate structures via convex optimization with applications in computer vision and pattern recognition Automatic face recognition from frontal images Face alignment using intrinsic information Improving face gender classification by adding deliberately misaligned faces to the training data Real-time head pose estimation for driver assistance system using low-cost on-board computer Response to name: A dataset and a multimodal machine learning framework towards autism study Face Alignment via Regressing Local Binary Features 3-D Generic Elastic Models for Fast and Texture Preserving 2-D Novel Pose Synthesis Face image retrieval by shape manipulation An algorithm for automatic skin smoothing in digital portraits Nonlinear 3D Face Morphable Model Image Super-resolution via Feature-augmented Random Forest Cascade R-CNN: Delving Into High Quality Object Detection Deep Multi-Center Learning for Face Alignment Real-Time Age-Invariant Face Recognition in Videos Using the ScatterNet Inception Hybrid Network (SIHN) Efficient face recognition with compensation for aging variations AgeDB: The First Manually Collected, In-the-Wild Age Database Distance metric optimization driven convolutional neural network for age invariant face recognition Deep Learning and Modelling of Audio-, Visual-, and Multimodal Audio-Visual Data in Brain-Inspired SNN Face Recognition Based on Local Gabor Binary Patterns and Convolutional Neural Network A SURVEY ON AGE INVARIANT FACE RECOGNITION METHODS Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition Longitudinal Study of Automatic Face Recognition How Does Aging Affect Facial Components? Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition Human Face Recognition Using Superior Principal Component Analysis (SPCA) The Research of Facial Feature Extraction and Recognition Based on Curvelet An Improved Model for Imperfect Facial Recognition using Python-Open CV Deep Learning For Smile Recognition Facial Boundary Image Reconstruction Using Elliptical Approximation and Convex Hull Face inpainting based on high-level facial attributes Face inpainting based on high-level facial attributes Facilitating fashion camouflage art
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