In this paper , we have surveyed various feature extraction methods , which is the success key to Facial Expression Recognition ( FER ) . Facial Expression Recognition has light volume because the facial image , facial occlusion , faces colour / shape etc is not an easy to deal with . In this paper , we have presented few appearance based techniques like Gabor Filter , Principal Component Analysis ( PCA ) , Local binary patterns ( LBP ) , Linear Discriminant Analysis ( LDA ) , with different classifiers like a Support Vector Machine ( SVM ) , Artificial Neural Network ( ANN ) , and fuzzy logic , which are used to recognize human expression in various conditions on different databases . Secondly , a new approach for automatic hidden sadness detection algorithm is proposed . Finally , Support Vector Machine and Random Forest classifiers are applied , since it has been shown that they provide state-of-the-art accuracy for the facial expression recognition problem . Two experiments were conducted , one with all extracted features from the face , and the other with only eye region features . Also , when the distance from the camera was 50 cm , it was confirmed that a depth noise of 1 mm maximum occurred . As a result , it is suggested that unconditional trust in depth information may be adversely affected in facial expression recognition process through structured-light type RGB-D camera . In this work we present the evolution in facial image data sets and the methodologies for facial identification and recognition of expressions such as anger , sadness , happiness , disgust , fear and surprise . We observe that most of the earlier methods for facial and expression recognition aimed at improving the recognition rates for facial feature-based methods using static images . However , the recent methodologies have shifted focus towards robust implementation of facial/expression recognition from large image databases that vary with space ( gathered from the internet ) and time ( video recordings ) . The automatic detection of the activation of facial muscles , i.e. the detection of the so called facial Action Units ( AUs ) , has received significant attention due to the application of facial expression analysis/recognition in areas such as affect recognition or behavior analysis . However , the recognition of subtle expressions is a challenging task that requires a multimodal approach where several sources of information are used . The overall accuracy achieved with the proposed hierarchical system was above 93 % . The work shows the strong correlation between language/culture and type of emotion , and can further be extended to other scenarios such as gender-based recognition , facial-expression based recognition , age-based recognition … etc . Based on the image analysis of human facial expression , a model of facial image based on constituents is built for facial expression image recognition in this paper . The decision tree of facial expression image is proposed according to the features of particular constituents , such as the shape and position . Facial expression recognition analysis is one of the most challenging research areas in the field of computer vision . Though face exhibits different facial expressions - which can be instantly recognized by human eyes - it is very difficult for a computer to extract and use the information content from these expressions . Both humans and computer algorithms can greatly benefit from being able to classify facial expressions . Possible applications of automatic facial expression recognition include better transcription of videos , movie or advertisement recommendations , detection of pain in telemedicine etc . Traditional convolutional neural networks ( CNNs ) that use convolutional layers , max or average pooling and fully connected layers are considered to capture only first - order statistics . In this work , we experiment with using manifold networks for pooling per - frame features . In summary , the contribution of this paper is two - fold : End - to - end pooling of second - order statistics for both videos and images in the context of facial expression recognition State - of - art result on image - based facial expression recognition section : Related Works subsection : Overview Facial expression is localized in the facial region whereas images in the wild contain large irrelevant information . Due to this , face detection is performed first and then aligned based on facial landmark locations . paragraph : Covariance Matrix for Spatial Pooling : In order to apply covariance pooling to image - based facial expression recognition problem , as shown in Figure [ reference ] , outputs from final convolutional layers can be flattened and used to compute covariance matrix . Let be the output obtained after several convolutional layers , where stand for width , height and number of channels in the output respectively . The proposed architecture achieves % for CKP and % for MMI , therefore performing better than the state of the art using CNNs . Automatic facial expression recognition has a broad spectrum of applications such as human - computer interaction and safety systems . This is due to the fact that non - verbal cues are important forms of communication and play a pivotal role in interpersonal communication . Historically , there have been several major approaches to face modeling , with two primary ones being discriminative / landmark - based approaches and parameterized appearance models , or PAMs ( see for additional discussion ) . In recent years , methods which directly extract 2D facial keypoints using modern deep learning tools have become the dominant approach to facial expression analysis , due to their flexibility and robustness to occlusions and extreme pose changes . A by - product of keypoint - based facial expression analysis is the ability to recover the 3D pose of the head , by establishing correspondence between the keypoints and a 3D head model and performing alignment . Although these features are chosen automatically , they are justifiable when inspected individually . It can be said that the movements of the mouth and eyebrows are particularly effective in recognizing facial expressions . Three of the features describe the movements of the eyebrows , while four describe movements of the mouth . In 2019 , Facial Micro-Expression Grand Challenge combines three popular datasets , i.e. SMIC , CASME II , and SAMM into a single cross-database which requires the generalization of proposed method on a wider range of subject characteristics . In this paper , we propose a simple yet effective CapsuleNet for micro-expression recognition . The effectiveness of our proposed methods was evaluated on the cross-database micro-expression benchmark using the Leave-One-Object-Out cross-validation . Micro-expression analysis has various potential applications in public security and clinical medicine . Researches are focused on the automatic micro-expression recognition , because it is hard to recognize the micro-expression by the naked eye . This research proposes a novel algorithm for automatic micro-expression analysis which combines a deep multi-task convolutional network for detecting the facial landmarks and a fused deep convolutional network for estimating the optical flow features of the micro-expression . Finally , a revised optical flow feature is applied for refining the information of the features and a Support Vector Machine classifier is adopted for recognizing and detecting the micro-expression . The result of experiments on two spontaneous micro-expression database proves that our method achieved competitive performance in micro-expression recognition and detection . Due to the low quality of captured images , the existing algorithms are not able to perform as well as expected . For addressing this problem , we conduct a comprehensive study about the micro-expression recognition problem under low-resolution cases with face hallucination method . The experimental results show that the proposed framework obtains promising results on micro-expression recognition under low-resolution cases . In this paper we present a novel Spontaneous Micro-expression Database SMIC , which includes 164 micro-expression video clips elicited from 16 participants . Micro-expression detection and recognition performance are provided as baselines . SMIC provides sufficient source material for comprehensive testing of automatic systems for analyzing micro-expressions , which has not been possible with any previously published database . Micro-expression , for its high objectivity in emotion detection , has emerged to be a promising modality in affective computing . Recently , deep learning methods have been successfully introduced into the micro-expression recognition area . Whilst the higher recognition accuracy achieved , substantial challenges in micro-expression recognition remain . Recently , Generative Adversarial Networks are popularly used in face generation and get the state-of-art result . However , it 's hard to swap the local area of face while many of previous work has focused on either generating face from a noise vector which belongs to some kind of data distribution or swaps the whole face . Providing methods to support audio-visual interaction with growing volumes of video data is an increasingly important challenge for data mining . To this end , there has been some success in speech-driven lip motion generation or talking face generation . Among them , talking face generation aims to generate realistic talking heads synchronized with the audio or text input . Visual speech synthesis will surely make the machine more human-friendly . The modelling and synthesis of the movements of visible articulators such as lips are predominant components in the production pipeline for talking face synthesis , especially in speech animation . Lip being the most dynamic visible articulator lip modelling is attempted as a separate module in many works . We have proposed a technique for synthesizing natural talking-face animation that permits such communications . How to evaluate the performance of talking-face animation , however , has remained an outstanding issue . The performance of talking-face animation is determined in three parameters : ( 1 ) Does it reproduce human talking to an extent that permits lipreading ? Constructing a talking head model of a person allows generation of a novel talking face animation from an unseen audio sequence of the person . This has important applications such as building virtual avatars of people that can interact with real people in novel situations , model-based video compression , teleconferencing , human-computer interaction , computer graphics and video games . Not only does this play a crucial role in building the dataset that forms the basis of much of the research done in this thesis , the method learns powerful representations of the visual and auditory inputs which can be used for related tasks such as lip reading . We also show that the methods developed here can be extended to the problem of generating talking faces from audio and still images . We then propose a number of deep learning models that are able to recognise visual speech at word and sentence level . The goal of our work is to complete missing areas of images of talking faces , exploiting information from both the visual and audio modalities . Existing image inpainting methods rely solely on visual content that does n’t always provide sufficient information for the task . All previous studies of face recognition in newborns used schematic faces , photographs or static real faces . In our study , we used video films to explore , for the first time , recognition of talking faces , in newborns . Our results suggest that video films of talking faces are very salient stimuli for newborns and can enhance face recognition a few hours after birth . This paper presents an acoustic viseme based continuous speech recognition system for speech driven talking face animation . The system is developed using viseme HMMs with acoustic speech as input only . Indeed , not only does it contain both voice and face modalities , but it also integrates the combined dynamics of voice and lip motion . First , an overview of the state of the art in the field of talking faces is given . The benchmarking evaluation framework for talking-face modality is then introduced . Deep convolutional neural networks ( CNNs ) not only have achieved unprecedented success in versatile high level vision problems , but also exhibited their remarkable power in understanding , generating , and recovering images . In this section , we focus on the task of facial attribute transfer , and briefly survey the CNN models for image generation and face generation . subsection : CNN for image generation In comparison , our DIAT model can achieve much better transfer results than the competing methods . Besides , CNNs have also been developed for other face generation tasks . For painting style transfer of head portrait , Selim et al . modify the perceptual loss to balance the contribution of the input photograph and the aligned exemplar painting . In this paper , a regularized kernel locality preserving discriminant analysis ( RKLPDA ) method is proposed for facial feature extraction and recognition . The proposed RKLPDA comes into the characteristic of LPDA that encodes both the geometrical and discriminant structure of the data manifold , and improves the classification ability for linear non-separable data by introducing kernel trick . This paper proposes a regularized locality preserving discriminant analysis ( RLPDA ) approach for facial feature extraction and recognition . The RLPDA approach decomposes the eigenspace of the locality preserving within-class scatter matrix into three subspaces , i.e. , the face space , the noise space and the null space , and then regularizes the three subspaces differently according to their predicted eigenvalues .
In this paper , we have surveyed various feature extraction methods , which is the success key to Facial Expression Recognition ( FER ) . Facial Expression Recognition has light volume because the facial image , facial occlusion , faces colour / shape etc is not an easy to deal with . In this paper , we have presented few appearance based techniques like Gabor Filter , Principal Component Analysis ( PCA ) , Local binary patterns ( LBP ) , Linear Discriminant Analysis ( LDA ) , with different classifiers like a Support Vector Machine ( SVM ) , Artificial Neural Network ( ANN ) , and fuzzy logic , which are used to recognize human expression in various conditions on different databases .
Secondly , a new approach for automatic hidden sadness detection algorithm is proposed . Finally , Support Vector Machine and Random Forest classifiers are applied , since it has been shown that they provide state-of-the-art accuracy for the facial expression recognition problem . Two experiments were conducted , one with all extracted features from the face , and the other with only eye region features .
Also , when the distance from the camera was 50 cm , it was confirmed that a depth noise of 1 mm maximum occurred . As a result , it is suggested that unconditional trust in depth information may be adversely affected in facial expression recognition process through structured-light type RGB-D camera .
In this work we present the evolution in facial image data sets and the methodologies for facial identification and recognition of expressions such as anger , sadness , happiness , disgust , fear and surprise . We observe that most of the earlier methods for facial and expression recognition aimed at improving the recognition rates for facial feature-based methods using static images . However , the recent methodologies have shifted focus towards robust implementation of facial/expression recognition from large image databases that vary with space ( gathered from the internet ) and time ( video recordings ) .
The automatic detection of the activation of facial muscles , i.e. the detection of the so called facial Action Units ( AUs ) , has received significant attention due to the application of facial expression analysis/recognition in areas such as affect recognition or behavior analysis . However , the recognition of subtle expressions is a challenging task that requires a multimodal approach where several sources of information are used .
The overall accuracy achieved with the proposed hierarchical system was above 93 % . The work shows the strong correlation between language/culture and type of emotion , and can further be extended to other scenarios such as gender-based recognition , facial-expression based recognition , age-based recognition … etc .
Based on the image analysis of human facial expression , a model of facial image based on constituents is built for facial expression image recognition in this paper . The decision tree of facial expression image is proposed according to the features of particular constituents , such as the shape and position .
Facial expression recognition analysis is one of the most challenging research areas in the field of computer vision . Though face exhibits different facial expressions - which can be instantly recognized by human eyes - it is very difficult for a computer to extract and use the information content from these expressions .
Both humans and computer algorithms can greatly benefit from being able to classify facial expressions . Possible applications of automatic facial expression recognition include better transcription of videos , movie or advertisement recommendations , detection of pain in telemedicine etc . Traditional convolutional neural networks ( CNNs ) that use convolutional layers , max or average pooling and fully connected layers are considered to capture only first - order statistics .
In this work , we experiment with using manifold networks for pooling per - frame features . In summary , the contribution of this paper is two - fold : End - to - end pooling of second - order statistics for both videos and images in the context of facial expression recognition State - of - art result on image - based facial expression recognition section : Related Works
subsection : Overview Facial expression is localized in the facial region whereas images in the wild contain large irrelevant information . Due to this , face detection is performed first and then aligned based on facial landmark locations .
paragraph : Covariance Matrix for Spatial Pooling : In order to apply covariance pooling to image - based facial expression recognition problem , as shown in Figure [ reference ] , outputs from final convolutional layers can be flattened and used to compute covariance matrix . Let be the output obtained after several convolutional layers , where stand for width , height and number of channels in the output respectively .
The proposed architecture achieves % for CKP and % for MMI , therefore performing better than the state of the art using CNNs . Automatic facial expression recognition has a broad spectrum of applications such as human - computer interaction and safety systems . This is due to the fact that non - verbal cues are important forms of communication and play a pivotal role in interpersonal communication .
Historically , there have been several major approaches to face modeling , with two primary ones being discriminative / landmark - based approaches and parameterized appearance models , or PAMs ( see for additional discussion ) . In recent years , methods which directly extract 2D facial keypoints using modern deep learning tools have become the dominant approach to facial expression analysis , due to their flexibility and robustness to occlusions and extreme pose changes . A by - product of keypoint - based facial expression analysis is the ability to recover the 3D pose of the head , by establishing correspondence between the keypoints and a 3D head model and performing alignment .
Although these features are chosen automatically , they are justifiable when inspected individually . It can be said that the movements of the mouth and eyebrows are particularly effective in recognizing facial expressions . Three of the features describe the movements of the eyebrows , while four describe movements of the mouth .
In 2019 , Facial Micro-Expression Grand Challenge combines three popular datasets , i.e. SMIC , CASME II , and SAMM into a single cross-database which requires the generalization of proposed method on a wider range of subject characteristics . In this paper , we propose a simple yet effective CapsuleNet for micro-expression recognition . The effectiveness of our proposed methods was evaluated on the cross-database micro-expression benchmark using the Leave-One-Object-Out cross-validation .
Micro-expression analysis has various potential applications in public security and clinical medicine . Researches are focused on the automatic micro-expression recognition , because it is hard to recognize the micro-expression by the naked eye . This research proposes a novel algorithm for automatic micro-expression analysis which combines a deep multi-task convolutional network for detecting the facial landmarks and a fused deep convolutional network for estimating the optical flow features of the micro-expression .
Finally , a revised optical flow feature is applied for refining the information of the features and a Support Vector Machine classifier is adopted for recognizing and detecting the micro-expression . The result of experiments on two spontaneous micro-expression database proves that our method achieved competitive performance in micro-expression recognition and detection .
Due to the low quality of captured images , the existing algorithms are not able to perform as well as expected . For addressing this problem , we conduct a comprehensive study about the micro-expression recognition problem under low-resolution cases with face hallucination method . The experimental results show that the proposed framework obtains promising results on micro-expression recognition under low-resolution cases .
In this paper we present a novel Spontaneous Micro-expression Database SMIC , which includes 164 micro-expression video clips elicited from 16 participants . Micro-expression detection and recognition performance are provided as baselines . SMIC provides sufficient source material for comprehensive testing of automatic systems for analyzing micro-expressions , which has not been possible with any previously published database .
Micro-expression , for its high objectivity in emotion detection , has emerged to be a promising modality in affective computing . Recently , deep learning methods have been successfully introduced into the micro-expression recognition area . Whilst the higher recognition accuracy achieved , substantial challenges in micro-expression recognition remain .
Recently , Generative Adversarial Networks are popularly used in face generation and get the state-of-art result . However , it 's hard to swap the local area of face while many of previous work has focused on either generating face from a noise vector which belongs to some kind of data distribution or swaps the whole face .
Providing methods to support audio-visual interaction with growing volumes of video data is an increasingly important challenge for data mining . To this end , there has been some success in speech-driven lip motion generation or talking face generation . Among them , talking face generation aims to generate realistic talking heads synchronized with the audio or text input .
Visual speech synthesis will surely make the machine more human-friendly . The modelling and synthesis of the movements of visible articulators such as lips are predominant components in the production pipeline for talking face synthesis , especially in speech animation . Lip being the most dynamic visible articulator lip modelling is attempted as a separate module in many works .
We have proposed a technique for synthesizing natural talking-face animation that permits such communications . How to evaluate the performance of talking-face animation , however , has remained an outstanding issue . The performance of talking-face animation is determined in three parameters : ( 1 ) Does it reproduce human talking to an extent that permits lipreading ?
Constructing a talking head model of a person allows generation of a novel talking face animation from an unseen audio sequence of the person . This has important applications such as building virtual avatars of people that can interact with real people in novel situations , model-based video compression , teleconferencing , human-computer interaction , computer graphics and video games .
Not only does this play a crucial role in building the dataset that forms the basis of much of the research done in this thesis , the method learns powerful representations of the visual and auditory inputs which can be used for related tasks such as lip reading . We also show that the methods developed here can be extended to the problem of generating talking faces from audio and still images . We then propose a number of deep learning models that are able to recognise visual speech at word and sentence level .
The goal of our work is to complete missing areas of images of talking faces , exploiting information from both the visual and audio modalities . Existing image inpainting methods rely solely on visual content that does n’t always provide sufficient information for the task .
All previous studies of face recognition in newborns used schematic faces , photographs or static real faces . In our study , we used video films to explore , for the first time , recognition of talking faces , in newborns . Our results suggest that video films of talking faces are very salient stimuli for newborns and can enhance face recognition a few hours after birth .
This paper presents an acoustic viseme based continuous speech recognition system for speech driven talking face animation . The system is developed using viseme HMMs with acoustic speech as input only .
Indeed , not only does it contain both voice and face modalities , but it also integrates the combined dynamics of voice and lip motion . First , an overview of the state of the art in the field of talking faces is given . The benchmarking evaluation framework for talking-face modality is then introduced .
Deep convolutional neural networks ( CNNs ) not only have achieved unprecedented success in versatile high level vision problems , but also exhibited their remarkable power in understanding , generating , and recovering images . In this section , we focus on the task of facial attribute transfer , and briefly survey the CNN models for image generation and face generation . subsection : CNN for image generation
In comparison , our DIAT model can achieve much better transfer results than the competing methods . Besides , CNNs have also been developed for other face generation tasks . For painting style transfer of head portrait , Selim et al . modify the perceptual loss to balance the contribution of the input photograph and the aligned exemplar painting .
In this paper , a regularized kernel locality preserving discriminant analysis ( RKLPDA ) method is proposed for facial feature extraction and recognition . The proposed RKLPDA comes into the characteristic of LPDA that encodes both the geometrical and discriminant structure of the data manifold , and improves the classification ability for linear non-separable data by introducing kernel trick .
This paper proposes a regularized locality preserving discriminant analysis ( RLPDA ) approach for facial feature extraction and recognition . The RLPDA approach decomposes the eigenspace of the locality preserving within-class scatter matrix into three subspaces , i.e. , the face space , the noise space and the null space , and then regularizes the three subspaces differently according to their predicted eigenvalues .
A Literature Survey on Facial Expression Recognition techniques using Appearance based features Automatic Hidden Sadness Detection Using Micro-Expressions Comparison of facial expression recognition performance according to the use of depth information of structured-light type RGB-D camera A Survey of the Trends in Facial and Expression Recognition Databases and Methods Fusing Multilabel Deep Networks for Facial Action Unit Detection A Two-Stage Hierarchical Multilingual Emotion Recognition System Using Hidden Markov Models and Neural Networks ANALYSIS AND RECOGNITION OF FACIAL EXPRESSION IMAGE FACIAL EXPRESSION RECOGNITION WITH PCA USING DIFFERENT DATABASE Covariance Pooling for Facial Expression Recognition DeXpression: Deep Convolutional Neural Network for Expression Recognition Fine-Grained Head Pose Estimation Without Keypoints Greedy search for descriptive spatial face features CapsuleNet for Micro-Expression Recognition Micro-expression Analysis by Fusing Deep Convolutional Neural Network and Optical Flow Micro-expression Recognition Under Low-resolution Cases A Spontaneous Micro-expression Database: Inducement, collection and baseline Micro-Attention for Micro-Expression recognition Cycle-Consistent Adversarial Network for Facial Local-Region Exchange in Wild Mining Audio, Text and Visual Information for Talking Face Generation Lip Motion Synthesis for Speech Animation Using Active Shape Model Subjective evaluation of a synthetic talking face in an acoustically noisy environment A review on data-driven learning of a talking head model Visual recognition of human communication Audio-Assisted Image Inpainting for Talking Faces Auditory-visual perception of talking faces at birth: a new paradigm Visualize speech: a continuous speech recognition system for facial animation using acoustic visemes Talking-face Verification Deep Identity-aware Transfer of Facial Attributes Regularized Kernel Locality Preserving Discriminant Analysis for Face Recognition Regularized locality preserving discriminant analysis for face recognition
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FG FG 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) FG CoDIT VISIGRAPP (5: VISAPP) FG BigCom ICDM ICASSP AVSP ArXiv ACIVS (2)
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