Most successful methods in both challenges focus on spoof-aware features , rather than focusing on a powerful classifier . In this paper we present a novel raw waveform based deep model for spoofing detection , which jointly acts as a feature extractor and classifier , thus allowing it to directly classify speech signals . This approach can be considered as an end-to-end classifier , which removes the need for any pre- or post-processing on the data , making training and evaluation a streamlined process , consuming less time than other neural-network based approaches . Cross-database experiments have , however , revealed that the performance of the state-of-the-art methods drops drastically as they fail to cope with new attacks scenarios and other operating conditions that have not been seen during training and development phases . So far , even the popular convolutional neural networks ( CNN ) have failed to derive well-generalizing features for face anti-spoofing . In this work , we explore the effect of different factors , such as acquisition conditions and presentation attack instrument ( PAI ) variation , on the generalization of color texture-based face anti-spoofing . Therefore , a reliable way to detect malicious attacks is crucial to the robustness of the face recognition system . This paper describes a new approach to utilizing light field camera for defending spoofing face attacks , like ( warped ) printed 2D facial photos and high-definition tablet images . The light field camera is a sensor that can record the directions as well as the colors of incident rays . Face recognition has been extensively used in a wide variety of security systems for identity authentication for years . However , many security systems are vulnerable to spoofing face attacks ( e.g. , 2D printed photo , replayed video ) . Consequently , a number of anti-spoofing approaches have been proposed . This paper is among the first to use defense methods to improve the robustness of ASV spoofing countermeasure models under adversarial attacks . The experimental results show that these two defense methods positively help spoofing countermeasure models counter adversarial examples . Spoofing could pose a major threat for GPS navigation systems , so the GPS users have to gain an in-depth understanding of GPS spoofing . Therefore , spoofing countermeasure is a significant subject of research these days . In this paper , we utilize wavelets transform ( WT ) as a tool that effectively hinders the adverse activities of these groups , and then introduce two novel methods to mitigate spoofing in the acquisition and tracking processes . Spoofing face recognition systems with photos or videos of someone else is not difficult . Sometimes , all one needs is to display a picture on a laptop monitor or a printed photograph to the biometric system . Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud , cyberbullying , sextorsion , etc . In this work , a face recognition system is proposed , with an application to the spoofing prevention . The method is based on the Histogram of Oriented Gradients ( HOG ) descriptor . In the last years , the topic of ECG-based biometrics has quickly evolved toward the commercial applications , mainly by addressing the reduced acceptability and comfort by proposing new off-the-person , wearable , and seamless acquisition settings . Furthermore , researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics , as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness . In this paper , we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods , signal datasets , and publicly available ECG collections . Existing countermeasures against spoofing adopt face texture analysis , motion detection and surface reflection analysis . For the purpose of face anti-spoofing analysis , skin structure is a key factor in achieving the target of our study . Skin consists of multiple layers structure which produces multiple reflections : surface and subsurface reflections . Third , all features are combined as the global descriptor of the face image , then fed into an SVM classifier to verify the anti‐spoofing detection . Experimental results show that the proposed strategy can effectively enhance the performance , concerning with the accuracy of spoofed face detection in four widely used anti‐spoofing databases . With the development of mobile devices , it is hopeful and pressing to deploy face recognition and face anti-spoofing ( FAS ) model on cell phone or portable devices . Most of existing face anti-spoofing methods focus on building computational costly detector for better spoofing face detection performance . However , these detectors are unfriendly to be deployed on the mobile device for real-time FAS applications . Imposters gain unauthorized access to biometric recognition systems using fake biometric data of the legitimate user termed as spoofing . Spoofing of face recognition systems is done by photographs , 3D models and videos of the user . Attack video contains noise from the acquisition process . We also design a method to construct a dataset containing relatively -labelled image pairs whose individual images are sampled from the SCUT-FBP benchmark database . The experimental results on the SCUT-FBP benchmark show that our $ \mathbf{R}^{2}$ -ResNeXt achieves the state-of-the-art performance compared with related literatures , and further indicates the effectiveness of the deep residual architecture and relative beauty ranking into regression task for facial beauty prediction . It is very hard to apply a well defined concept to describe the attractiveness of a face . In fact , facial attractiveness prediction is a label ambiguity problem . In order to solve the problem , we propose a novel deep architecture called Deep Adaptive Label Distribution Learning ( DALDL ) . A novel geometric facial beauty ( GFB ) database is provided in this paper . Facial beauty analysis has been an emerging subject of multimedia and biometrics . This paper aims at exploring the essence of facial beauty from the viewpoint of geometric characteristic toward an interactive attractiveness assessment ( IAA ) application . To that effect , we propose Photofeeler-D3 - the first convolutional neural network as accurate as 10 human votes for how smart , trustworthy , and attractive the subject appears in highly variable dating photos . Our " attractive " output is also applicable to Facial Beauty Prediction ( FBP ) , making Photofeeler-D3 state-of-the-art for both DPIP and FBP . We achieve this by leveraging Photofeeler 's Dating Dataset ( PDD ) with over 1 million images and tens of millions of votes , our novel technique of voter modeling , and cutting-edge computer vision techniques . Because of the lack of discriminative face representations and scarcity of labeled training data , facial beauty prediction ( FBP ) , which aims at assessing facial attractiveness automatically , has become a challenging pattern recognition problem . Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features , BeautyNet for unconstrained facial beauty prediction is proposed in this paper . The best Pearson correlation 0.8187 was achieved by the CNN model . The results of the experiments indicate that the SCUT-FBP dataset provides a reliable benchmark for facial beauty perception . Experimental results show its superiority to state-of-the-art geometric based face beautification methods . Moreover , the mainstream hypotheses on facial beauty perception ( e.g. , the averageness , symmetry , and golden ratio hypotheses ) are proved to be compatible with the proposed hypothesis . To this end , the existing schemes simply adopt a direct feature mapping , which relies on handcraft-designed low-level features that can not reflect human-level aesthetic perception . In this paper , we present a systematic framework towards designing biology-inspired , discriminative representation for facial aesthetic prediction . First , we design a group of biological experiments that adopt eye tracker to identify spatial regions of interest during the facial aesthetic judgments of subjects , which forms a Bio-inspired Facial Aesthetic Ontology ( Bio-FAO ) and is made public available . To the best of our knowledge , this is the first work to address learning to rank problem for facial beauty by CNN . We proved both the RankNet CNN and the RankSVM CNN are effective and excellent for facial beauty assessment . With a small amount of data , our approaches perform better than any other shallow models we have known . A number of studies empirically investigated how facial attractiveness was influenced by 2D facial characteristics , such as symmetry , averageness and golden ratio . However , few implementations of facial beauty assessment were based on 3D facial features . The purpose of this paper is to propose a novel cluster assessment system for facial attractiveness that is characterized by the incorporation of 3D geometric Moire features with an adjusted fuzzy neural network ( FNN ) . This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem . The method constructs a convolutional neural network of facial beauty prediction using a new deep cascaded fine-turning scheme with various face inputting channels , such as the original RGB face image , the detail layer image , and the lighting layer image . In this paper , we develop an automated Chinese female facial beauty classification system through the application of machine learning algorithm of SVM ( Support Vector Machine ) . We present a simple but effective feature extraction for facial beauty classification . 17 geometric features are designed to abstractly represent each facial image . The influence of three-dimensional facial contour and dynamic evaluation decoding on factors of smile esthetics is essential for facial beauty improvement . However , the kinematic features of the facial smile contour and the contribution from the soft tissue and underlying skeleton are uncharted . Facial action unit ( AU ) detection from images and videos is a challenging research topic and has attracted great attention in the past few years . This paper presents a novel method , task-dependent multi-task multiple kernel learning ( TD-MTMKL ) , to jointly detect the absence and presence of multiple AUs . Even though these representations are somewhat similar , they also have distinct differences as the Log-Gabor filters mimic the simple cells in the visual cortex while the Gabor energy filters emulate the complex cells , which causes subtle differences in the responses . In this paper , we analyze the difference between these two Gabor representations and quantify these differences on the task of facial action unit ( AU ) detection . In our experiments conducted on the Cohn-Kanade dataset , we report an average area underneath the ROC curve ( A ` ) of 92.60 % across 17 AUs for the Gabor energy filters , while the Log-Gabor representation achieved an average A ` of 96.11 % . We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues . Using OpenFace with facial action unit recognition , we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile . We then perform a lexical analysis on the spoken words , emphasizing the use of pauses and utterance breaks , feeding that to a Support Vector Machine to test deceit or truth prediction . We train classifiers with the multi-label with missing labels ( MLML ) learning framework by enforcing the consistency between the predicted labels and the provided labels as well as the local smoothness among the label assignments . Experiments on three benchmark data sets in image annotation and one benchmark data set in facial action unit recognition demonstrate the improved performance of our method in comparison of several state-of-the-art methods . HighlightsAn inductive method is proposed to handle missing labels in multi-label learning . The spatial domain texture involves image textures comparable to the actual texture and the dynamic texture synthesis involves videos which are given dynamic textures extended in a spatial or temporal domain . Facial actions cause local appearance changes over time , and thus dynamic texture descriptors should inherently be more suitable for facial action detection than their static variants . A video sequence is defined as a spatial temporal collection of texture in the temporal domain where dynamic features are extracted . This paper introduces recent advances in automatic facial action unit recognition , focusing on the fundamental components of face detection and registration , facial action representation , feature selection and classification . We discussed the role of each component , main techniques with their characteristics , challenges and potential directions of facial action unit analysis . Facial action unit recognition involves AUs occurrence detection , AUs temporal segment detection and AUs intensity estimation . We discussed the role of each component , main techniques with their characteristics , challenges and potential directions of facial action unit analysis . This approach is compared experimentally with other popular feature-ranking methods , and with a Support Vector Classifier SVC . Experimental results on natural benchmark data and on a problem in facial action unit classification demonstrate that the MLP ensemble is relatively insensitive to the feature-ranking method , and simple ranking methods perform as well as more sophisticated schemes . The results are interpreted with the assistance of bias/variance of 0/1 loss function . Moreover , it also maps the features to higher discriminative space . The features are applied to support vector machine for the facial action unit intensity detection . The proposed approach is evaluated on the popularly accepted database : DISFA database and UNBC shoulder pain database . This paper describes an approach to the facial action units detections . The involved action units ( AU ) include AU1 ( Inner Brow Raiser ) , AU2 ( Outer Brow Raiser ) , AU4 ( Brow Lowerer ) , AU6 ( Cheek Raise ) , AU12 ( Lip Corner Puller ) , AU15 ( Lip Corner Depressor ) , AU20 ( Lip Stretcher ) , and AU25 ( Lip Part ) .
Most successful methods in both challenges focus on spoof-aware features , rather than focusing on a powerful classifier . In this paper we present a novel raw waveform based deep model for spoofing detection , which jointly acts as a feature extractor and classifier , thus allowing it to directly classify speech signals . This approach can be considered as an end-to-end classifier , which removes the need for any pre- or post-processing on the data , making training and evaluation a streamlined process , consuming less time than other neural-network based approaches .
Cross-database experiments have , however , revealed that the performance of the state-of-the-art methods drops drastically as they fail to cope with new attacks scenarios and other operating conditions that have not been seen during training and development phases . So far , even the popular convolutional neural networks ( CNN ) have failed to derive well-generalizing features for face anti-spoofing . In this work , we explore the effect of different factors , such as acquisition conditions and presentation attack instrument ( PAI ) variation , on the generalization of color texture-based face anti-spoofing .
Therefore , a reliable way to detect malicious attacks is crucial to the robustness of the face recognition system . This paper describes a new approach to utilizing light field camera for defending spoofing face attacks , like ( warped ) printed 2D facial photos and high-definition tablet images . The light field camera is a sensor that can record the directions as well as the colors of incident rays .
Face recognition has been extensively used in a wide variety of security systems for identity authentication for years . However , many security systems are vulnerable to spoofing face attacks ( e.g. , 2D printed photo , replayed video ) . Consequently , a number of anti-spoofing approaches have been proposed .
This paper is among the first to use defense methods to improve the robustness of ASV spoofing countermeasure models under adversarial attacks . The experimental results show that these two defense methods positively help spoofing countermeasure models counter adversarial examples .
Spoofing could pose a major threat for GPS navigation systems , so the GPS users have to gain an in-depth understanding of GPS spoofing . Therefore , spoofing countermeasure is a significant subject of research these days . In this paper , we utilize wavelets transform ( WT ) as a tool that effectively hinders the adverse activities of these groups , and then introduce two novel methods to mitigate spoofing in the acquisition and tracking processes .
Spoofing face recognition systems with photos or videos of someone else is not difficult . Sometimes , all one needs is to display a picture on a laptop monitor or a printed photograph to the biometric system .
Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud , cyberbullying , sextorsion , etc . In this work , a face recognition system is proposed , with an application to the spoofing prevention . The method is based on the Histogram of Oriented Gradients ( HOG ) descriptor .
In the last years , the topic of ECG-based biometrics has quickly evolved toward the commercial applications , mainly by addressing the reduced acceptability and comfort by proposing new off-the-person , wearable , and seamless acquisition settings . Furthermore , researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics , as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness . In this paper , we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods , signal datasets , and publicly available ECG collections .
Existing countermeasures against spoofing adopt face texture analysis , motion detection and surface reflection analysis . For the purpose of face anti-spoofing analysis , skin structure is a key factor in achieving the target of our study . Skin consists of multiple layers structure which produces multiple reflections : surface and subsurface reflections .
Third , all features are combined as the global descriptor of the face image , then fed into an SVM classifier to verify the anti‐spoofing detection . Experimental results show that the proposed strategy can effectively enhance the performance , concerning with the accuracy of spoofed face detection in four widely used anti‐spoofing databases .
With the development of mobile devices , it is hopeful and pressing to deploy face recognition and face anti-spoofing ( FAS ) model on cell phone or portable devices . Most of existing face anti-spoofing methods focus on building computational costly detector for better spoofing face detection performance . However , these detectors are unfriendly to be deployed on the mobile device for real-time FAS applications .
Imposters gain unauthorized access to biometric recognition systems using fake biometric data of the legitimate user termed as spoofing . Spoofing of face recognition systems is done by photographs , 3D models and videos of the user . Attack video contains noise from the acquisition process .
We also design a method to construct a dataset containing relatively -labelled image pairs whose individual images are sampled from the SCUT-FBP benchmark database . The experimental results on the SCUT-FBP benchmark show that our $ \mathbf{R}^{2}$ -ResNeXt achieves the state-of-the-art performance compared with related literatures , and further indicates the effectiveness of the deep residual architecture and relative beauty ranking into regression task for facial beauty prediction .
It is very hard to apply a well defined concept to describe the attractiveness of a face . In fact , facial attractiveness prediction is a label ambiguity problem . In order to solve the problem , we propose a novel deep architecture called Deep Adaptive Label Distribution Learning ( DALDL ) .
A novel geometric facial beauty ( GFB ) database is provided in this paper . Facial beauty analysis has been an emerging subject of multimedia and biometrics . This paper aims at exploring the essence of facial beauty from the viewpoint of geometric characteristic toward an interactive attractiveness assessment ( IAA ) application .
To that effect , we propose Photofeeler-D3 - the first convolutional neural network as accurate as 10 human votes for how smart , trustworthy , and attractive the subject appears in highly variable dating photos . Our " attractive " output is also applicable to Facial Beauty Prediction ( FBP ) , making Photofeeler-D3 state-of-the-art for both DPIP and FBP . We achieve this by leveraging Photofeeler 's Dating Dataset ( PDD ) with over 1 million images and tens of millions of votes , our novel technique of voter modeling , and cutting-edge computer vision techniques .
Because of the lack of discriminative face representations and scarcity of labeled training data , facial beauty prediction ( FBP ) , which aims at assessing facial attractiveness automatically , has become a challenging pattern recognition problem . Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features , BeautyNet for unconstrained facial beauty prediction is proposed in this paper .
The best Pearson correlation 0.8187 was achieved by the CNN model . The results of the experiments indicate that the SCUT-FBP dataset provides a reliable benchmark for facial beauty perception .
Experimental results show its superiority to state-of-the-art geometric based face beautification methods . Moreover , the mainstream hypotheses on facial beauty perception ( e.g. , the averageness , symmetry , and golden ratio hypotheses ) are proved to be compatible with the proposed hypothesis .
To this end , the existing schemes simply adopt a direct feature mapping , which relies on handcraft-designed low-level features that can not reflect human-level aesthetic perception . In this paper , we present a systematic framework towards designing biology-inspired , discriminative representation for facial aesthetic prediction . First , we design a group of biological experiments that adopt eye tracker to identify spatial regions of interest during the facial aesthetic judgments of subjects , which forms a Bio-inspired Facial Aesthetic Ontology ( Bio-FAO ) and is made public available .
To the best of our knowledge , this is the first work to address learning to rank problem for facial beauty by CNN . We proved both the RankNet CNN and the RankSVM CNN are effective and excellent for facial beauty assessment . With a small amount of data , our approaches perform better than any other shallow models we have known .
A number of studies empirically investigated how facial attractiveness was influenced by 2D facial characteristics , such as symmetry , averageness and golden ratio . However , few implementations of facial beauty assessment were based on 3D facial features . The purpose of this paper is to propose a novel cluster assessment system for facial attractiveness that is characterized by the incorporation of 3D geometric Moire features with an adjusted fuzzy neural network ( FNN ) .
This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem . The method constructs a convolutional neural network of facial beauty prediction using a new deep cascaded fine-turning scheme with various face inputting channels , such as the original RGB face image , the detail layer image , and the lighting layer image .
In this paper , we develop an automated Chinese female facial beauty classification system through the application of machine learning algorithm of SVM ( Support Vector Machine ) . We present a simple but effective feature extraction for facial beauty classification . 17 geometric features are designed to abstractly represent each facial image .
The influence of three-dimensional facial contour and dynamic evaluation decoding on factors of smile esthetics is essential for facial beauty improvement . However , the kinematic features of the facial smile contour and the contribution from the soft tissue and underlying skeleton are uncharted .
Facial action unit ( AU ) detection from images and videos is a challenging research topic and has attracted great attention in the past few years . This paper presents a novel method , task-dependent multi-task multiple kernel learning ( TD-MTMKL ) , to jointly detect the absence and presence of multiple AUs .
Even though these representations are somewhat similar , they also have distinct differences as the Log-Gabor filters mimic the simple cells in the visual cortex while the Gabor energy filters emulate the complex cells , which causes subtle differences in the responses . In this paper , we analyze the difference between these two Gabor representations and quantify these differences on the task of facial action unit ( AU ) detection . In our experiments conducted on the Cohn-Kanade dataset , we report an average area underneath the ROC curve ( A ` ) of 92.60 % across 17 AUs for the Gabor energy filters , while the Log-Gabor representation achieved an average A ` of 96.11 % .
We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues . Using OpenFace with facial action unit recognition , we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile . We then perform a lexical analysis on the spoken words , emphasizing the use of pauses and utterance breaks , feeding that to a Support Vector Machine to test deceit or truth prediction .
We train classifiers with the multi-label with missing labels ( MLML ) learning framework by enforcing the consistency between the predicted labels and the provided labels as well as the local smoothness among the label assignments . Experiments on three benchmark data sets in image annotation and one benchmark data set in facial action unit recognition demonstrate the improved performance of our method in comparison of several state-of-the-art methods . HighlightsAn inductive method is proposed to handle missing labels in multi-label learning .
The spatial domain texture involves image textures comparable to the actual texture and the dynamic texture synthesis involves videos which are given dynamic textures extended in a spatial or temporal domain . Facial actions cause local appearance changes over time , and thus dynamic texture descriptors should inherently be more suitable for facial action detection than their static variants . A video sequence is defined as a spatial temporal collection of texture in the temporal domain where dynamic features are extracted .
This paper introduces recent advances in automatic facial action unit recognition , focusing on the fundamental components of face detection and registration , facial action representation , feature selection and classification . We discussed the role of each component , main techniques with their characteristics , challenges and potential directions of facial action unit analysis .
Facial action unit recognition involves AUs occurrence detection , AUs temporal segment detection and AUs intensity estimation . We discussed the role of each component , main techniques with their characteristics , challenges and potential directions of facial action unit analysis .
This approach is compared experimentally with other popular feature-ranking methods , and with a Support Vector Classifier SVC . Experimental results on natural benchmark data and on a problem in facial action unit classification demonstrate that the MLP ensemble is relatively insensitive to the feature-ranking method , and simple ranking methods perform as well as more sophisticated schemes . The results are interpreted with the assistance of bias/variance of 0/1 loss function .
Moreover , it also maps the features to higher discriminative space . The features are applied to support vector machine for the facial action unit intensity detection . The proposed approach is evaluated on the popularly accepted database : DISFA database and UNBC shoulder pain database .
This paper describes an approach to the facial action units detections . The involved action units ( AU ) include AU1 ( Inner Brow Raiser ) , AU2 ( Outer Brow Raiser ) , AU4 ( Brow Lowerer ) , AU6 ( Cheek Raise ) , AU12 ( Lip Corner Puller ) , AU15 ( Lip Corner Depressor ) , AU20 ( Lip Stretcher ) , and AU25 ( Lip Part ) .
End-to-end spoofing detection with raw waveform CLDNNS On the generalization of color texture-based face anti-spoofing One-Snapshot Face Anti-spoofing Using a Light Field Camera Face liveness detection with feature discrimination between sharpness and blurriness Defense Against Adversarial Attacks on Spoofing Countermeasures of ASV Novel Anti-spoofing Methods Based on Discrete Wavelet Transform in the Acquisition and Tracking Stages of Civil GPS Receiver Face liveness detection under bad illumination conditions Entropy-Based Face Recognition and Spoof Detection for Security Applications Evolution, Current Challenges, and Future Possibilities in ECG Biometrics Face anti-spoofing countermeasure: Efficient 2D materials classification using polarization imaging Cover patches: A general feature extraction strategy for spoofing detection Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing Analysis of Spoofing Detection using Video Subsection Processing R2-ResNeXt: A ResNeXt-Based Regression Model with Relative Ranking for Facial Beauty Prediction Facial Attractiveness Prediction by Deep Adaptive Label Distribution Learning Facial beauty analysis based on geometric feature: Toward attractiveness assessment application Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception A New Hypothesis on Facial Beauty Perception Bio-Inspired Deep Attribute Learning Towards Facial Aesthetic Prediction Deep Rank Learning for Facial Attractiveness The cluster assessment of facial attractiveness using fuzzy neural network classifier based on 3D Moiré features A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model Automatic classification of Chinese female facial beauty using Support Vector Machine A novel three-dimensional smile analysis based on dynamic evaluation of facial curve contour Task-dependent multi-task multiple kernel learning for facial action unit detection Exploring visual features through Gabor representations for facial expression detection The Truth and Nothing But the Truth: Multimodal Analysis for Deception Detection Multi-label learning with missing labels for image annotation and facial action unit recognition VIDEO SEQUENCING BASED FACIAL EXPRESSION DETECTION WITH 3D LOCAL BINARY PATTERN VARIANTS Facial Representation for Automatic Facial Action Unit Analysis System A comprehensive survey on automatic facial action unit analysis Feature Ranking Ensembles for Facial Action Unit Classification Facial Action Unit Intensity Detection by Extracting Complimentary Information using Distance Metric Learning Multi-label Relation Modeling in Facial Action Units Detection
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