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Depth Videos for the Classification of Micro-Expressions

Ankith Jain Rakesh Kumar, Bir Bhanu, Christopher Casey, Sierra Cheung, Aaron Seitz

Auto-TLDR; RGB-D Dataset for the Classification of Facial Micro-expressions

Abstract Slides Poster

Facial micro-expressions are spontaneous, subtle, involuntary muscle movements occurring briefly on the face. The spotting and recognition of these expressions are difficult due to the subtle behavior, and the time duration of these expressions is about half a second, which makes it difficult for humans to identify them. These micro-expressions have many applications in our daily life, such as in the field of online learning, game playing, lie detection, and therapy sessions. Traditionally, researchers use RGB images/videos to spot and classify these micro-expressions, which pose challenging problems, such as illumination, privacy concerns and pose variation. The use of depth videos solves these issues to some extent, as the depth videos are not susceptible to the variation in illumination. This paper describes the collection of a first RGB-D dataset for the classification of facial micro-expressions into 6 universal expressions: Anger, Happy, Sad, Fear, Disgust, and Surprise. This paper shows the comparison between the RGB and Depth videos for the classification of facial micro-expressions. Further, a comparison of results shows that depth videos alone can be used to classify facial micro-expressions correctly in a decision tree structure by using the traditional and deep learning approaches with good classification accuracy. The dataset will be released to the public in the near future.

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Magnifying Spontaneous Facial Micro Expressions for Improved Recognition

Pratikshya Sharma, Sonya Coleman, Pratheepan Yogarajah, Laurence Taggart, Pradeepa Samarasinghe

Auto-TLDR; Eulerian Video Magnification for Micro Expression Recognition

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Building an effective automatic micro expression recognition (MER) system is becoming increasingly desirable in computer vision applications. However, it is also very challenging given the fine-grained nature of the expressions to be recognized. Hence, we investigate if amplifying micro facial muscle movements as a pre-processing phase, by employing Eulerian Video Magnification (EVM), can boost performance of Local Phase Quantization with Three Orthogonal Planes (LPQ-TOP) to achieve improved facial MER across various datasets. In addition, we examine the rate of increase for recognition to determine if it is uniform across datasets using EVM. Ultimately, we classify the extracted features using Support Vector Machines (SVM). We evaluate and compare the performance with various methods on seven different datasets namely CASME, CAS(ME)2, CASME2, SMIC-HS, SMIC-VIS, SMIC-NIR and SAMM. The results obtained demonstrate that EVM can enhance LPQ-TOP to achieve improved recognition accuracy on the majority of the datasets.

Responsive Social Smile: A Machine-Learning Based Multimodal Behavior Assessment Framework towards Early Stage Autism Screening

Yueran Pan, Kunjing Cai, Ming Cheng, Xiaobing Zou, Ming Li

Auto-TLDR; Responsive Social Smile: A Machine Learningbased Assessment Framework for Early ASD Screening

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which causes social deﬁcits in social lives. Early ASD screening for children is an important method to reduce the impact of ASD on people’s whole lives. Traditional screening methods rely on protocol experiments and subjective evaluations from clinicians and domain experts and thereby cost a lot. To standardize the process of ASD screening, we 1 collaborate with a group of ASD experts, and design a ”Responsive Social Smile” protocol and an experiment environment. Also, we propose a machine learningbased assessment framework for early ASD screening. By integrating technologies of speech recognition and computer vision, the framework can quantitatively analyze the behaviors of children under well-designed protocols. By collecting 196 test samples from 41 children in the clinical treatments, our proposed method obtains 85.20% accuracy for the score prediction of individual protocol, and 80.49% unweighted accuracy for the ﬁnal ASD prediction. This result indicates that our model reaches the average level of domain experts in ASD diagnosis.

Automatic Annotation of Corpora for Emotion Recognition through Facial Expressions Analysis

Alex Mircoli, Claudia Diamantini, Domenico Potena, Emanuele Storti

Auto-TLDR; Automatic annotation of video subtitles on the basis of facial expressions using machine learning algorithms

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The recent diffusion of social networks has made available an unprecedented amount of user-generated content, which may be analyzed in order to determine people's opinions and emotions about a large variety of topics. Research has made many efforts in defining accurate algorithms for analyzing emotions expressed by users in texts; however, their performance often rely on the existence of large annotated datasets, whose current scarcity represents a major issue. The manual creation of such datasets represents a costly and time-consuming activity and hence there is an increasing demand for techniques for the automatic annotation of corpora. In this work we present a methodology for the automatic annotation of video subtitles on the basis of the analysis of facial expressions of people in videos, with the goal of creating annotated corpora that may be used to train emotion recognition algorithms. Facial expressions are analyzed through machine learning algorithms, on the basis of a set of manually-engineered facial features that are extracted from video frames. The soundness of the proposed methodology has been evaluated through an extensive experimentation aimed at determining the performance on real datasets of each methodological step.

Pose-Based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation

Zhengyuan Yang, Amanda Kay, Yuncheng Li, Wendi Cross, Jiebo Luo

Auto-TLDR; Body Language Based Emotion Recognition for Psychiatric Symptoms Prediction

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Inspired by the human ability to infer emotions from body language, we propose an automated framework for body language based emotion recognition starting from regular RGB videos. In collaboration with psychologists, we further extend the framework for psychiatric symptom prediction. Because a specific application domain of the proposed framework may only supply a limited amount of data, the framework is designed to work on a small training set and possess a good transferability. The proposed system in the first stage generates sequences of body language predictions based on human poses estimated from input videos. In the second stage, the predicted sequences are fed into a temporal network for emotion interpretation and psychiatric symptom prediction. We first validate the accuracy and transferability of the proposed body language recognition method on several public action recognition datasets. We then evaluate the framework on a proposed URMC dataset, which consists of conversations between a standardized patient and a behavioral health professional, along with expert annotations of body language, emotions, and potential psychiatric symptoms. The proposed framework outperforms other methods on the URMC dataset.

Interpretable Emotion Classification Using Temporal Convolutional Models

Manasi Bharat Gund, Abhiram Ravi Bharadwaj, Ifeoma Nwogu

Auto-TLDR; Understanding the Dynamics of Facial Emotion Expression with Spatiotemporal Representations

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As with many problems solved by deep neural networks, existing solutions rarely explain, precisely, the important factors responsible for the predictions made by the model. This work looks to investigate how different spatial regions and landmark points change in position over time, to better explain the underlying factors responsible for various facial emotion expressions. By pinpointing the specific regions or points responsible for the classification of a particular facial expression, we gain better insight into the dynamics of the face when displaying that emotion. To accomplish this, we examine two spatiotemporal representations of moving faces, while expressing different emotions. The representations are then presented to a convolutional neural network for emotion classification. Class activation maps are used in highlighting the regions of interest and the results are qualitatively compared with the well known facial action units, using the facial action coding system. The model was originally trained and tested on the CK+ dataset for emotion classification, and then generalized to the SAMM dataset. In so doing, we successfully present an interpretable technique for understanding the dynamics that occur during convolutional-based prediction tasks on sequences of face data.

Identity-Aware Facial Expression Recognition in Compressed Video

Xiaofeng Liu, Linghao Jin, Xu Han, Jun Lu, Jonghye Woo, Jane You

Auto-TLDR; Exploring Facial Expression Representation in Compressed Video with Mutual Information Minimization

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This paper targets to explore the inter-subject variations eliminated facial expression representation in the compressed video domain. Most of the previous methods process the RGB images of a sequence, while the off-the-shelf and valuable expression-related muscle movement already embedded in the compression format. In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network. By enforcing the marginal independent of them, the expression feature is expected to be purer for the expression and be robust to identity shifts. Specifically, we propose a novel collaborative min-min game for mutual information (MI) minimization in latent space. We do not need the identity label or multiple expression samples from the same person for identity elimination. Moreover, when the apex frame is annotated in the dataset, the complementary constraint can be further added to regularize the feature-level game. In testing, only the compressed residual frames are required to achieve expression prediction. Our solution can achieve comparable or better performance than the recent decoded image based methods on the typical FER benchmarks with about 3$\times$ faster inference with compressed data.

Video-Based Facial Expression Recognition Using Graph Convolutional Networks

Daizong Liu, Hongting Zhang, Pan Zhou

Auto-TLDR; Graph Convolutional Network for Video-based Facial Expression Recognition

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Facial expression recognition (FER), aiming to classify the expression present in the facial image or video, has attracted a lot of research interests in the field of artificial intelligence and multimedia. In terms of video based FER task, it is sensible to capture the dynamic expression variation among the frames to recognize facial expression. However, existing methods directly utilize CNN-RNN or 3D CNN to extract the spatial-temporal features from different facial units, instead of concentrating on a certain region during expression variation capturing, which leads to limited performance in FER. In our paper, we introduce a Graph Convolutional Network (GCN) layer into a common CNN-RNN based model for video-based FER. First, the GCN layer is utilized to learn more contributing facial expression features which concentrate on certain regions after sharing information between nodes those represent CNN extracted features. Then, a LSTM layer is applied to learn long-term dependencies among the GCN learned features to model the variation. In addition, a weight assignment mechanism is also designed to weight the output of different nodes for final classification by characterizing the expression intensities in each frame. To the best of our knowledge, it is the first time to use GCN in FER task. We evaluate our method on three widely-used datasets, CK+, Oulu-CASIA and MMI, and also one challenging wild dataset AFEW8.0, and the experimental results demonstrate that our method has superior performance to existing methods.

Recognizing American Sign Language Nonmanual Signal Grammar Errors in Continuous Videos

Elahe Vahdani, Longlong Jing, Ying-Li Tian, Matt Huenerfauth

Auto-TLDR; ASL-HW-RGBD: Recognizing Grammatical Errors in Continuous Sign Language

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As part of the development of an educational tool that can help students achieve fluency in American Sign Language (ASL) through independent and interactive practice with immediate feedback, this paper introduces a near real-time system to recognize grammatical errors in continuous signing videos without necessarily identifying the entire sequence of signs. Our system automatically recognizes if a performance of ASL sentences contains grammatical errors made by ASL students. We first recognize the ASL grammatical elements including both manual gestures and nonmanual signals independently from multiple modalities (i.e. hand gestures, facial expressions, and head movements) by 3D-ResNet networks. Then the temporal boundaries of grammatical elements from different modalities are examined to detect ASL grammatical mistakes by using a sliding window-based approach. We have collected a dataset of continuous sign language, ASL-HW-RGBD, covering different aspects of ASL grammars for training and testing. Our system is able to recognize grammatical elements on ASL-HW-RGBD from manual gestures, facial expressions, and head movements and successfully detect 8 ASL grammatical mistakes.

Facial Expression Recognition Using Residual Masking Network

Luan Pham, Vu Huynh, Tuan Anh Tran

Auto-TLDR; Deep Residual Masking for Automatic Facial Expression Recognition

Abstract Slides Poster Similar

Automatic facial expression recognition (FER) has gained much attention due to its applications in human-computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the attention mechanism. We propose a novel Masking idea to boost the performance of CNN in facial expression task. It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions. In experiments, we combine the ubiquitous Deep Residual Network and Unet-like architecture to produce a Residual Masking Network. The proposed method holds state-of-the-art (SOTA) accuracy on the well-known FER2013 and private VEMO datasets. Our works are available on Github.

Siamese-Structure Deep Neural Network Recognizing Changes in Facial Expression According to the Degree of Smiling

Kazuaki Kondo, Taichi Nakamura, Yuichi Nakamura, Shin'Ichi Satoh

Auto-TLDR; A Siamese-Structure Deep Neural Network for Happiness Recognition

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A smile is a representative expression of happiness or high quality-of-life; however, automatic recognition of a smile according to happiness remains a challenging task. Because expressions of happiness are strongly dependent upon physical condition and occurrence of other emotions, and similar facial expression often occur under different emotions, we consider that there is no absolute visual pattern of a smile corresponding to happiness. Therefore, in this study, we assumed that a ``smile with happiness'' is observed as the temporal ascent in the degree of smiling and attempted to recognize this by capturing changes in facial expression within temporally sequential images. As an implementation of this scheme, we proposed a Siamese-structure deep neural network to compare facial expressions in two input images and estimate the existence of smile ascension or descension. For primal analysis of the proposed network, we developed a unique smiling dataset containing image pairs with various changes in smiling degree, including slight changes. The results demonstrated that the proposed method achieved nearly perfect recognition with >0.95 accuracy when recognizing changes in the degree of smiling that humans certainly recognize. Attention regions that contributed to the predicted labels were concentrated on the mouth, cheeks, and tail of the eyes, which indicates a reasonable function for recognizing changes in smiling degree was constructed by the proposed method.

Two-Level Attention-Based Fusion Learning for RGB-D Face Recognition

Hardik Uppal, Alireza Sepas-Moghaddam, Michael Greenspan, Ali Etemad

Auto-TLDR; Fused RGB-D Facial Recognition using Attention-Aware Feature Fusion

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With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results.

Learning Emotional Blinded Face Representations

Alejandro Peña Almansa, Julian Fierrez, Agata Lapedriza, Aythami Morales

Auto-TLDR; Blind Face Representations for Emotion Recognition

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This work proposes two new face representations that are blind to the expressions associated to emotional responses. This work is in part motivated by new international regulations for personal data protection, which force data controllers to protect any kind of sensitive information involved in automatic processes. The advances in affective computing have contributed to improve human-machine interfaces, but at the same time, the capacity to monitorize emotional responses trigger potential risks for humans, both in terms of fairness and privacy. We propose two different methods to learn these facial expression blinded features. We show that it is possible to eliminate information related to emotion recognition tasks, while the performance of subject verification, gender recognition, and ethnicity classification are just slightly affected. We also present an application to train fairer classifiers over a protected facial expression attribute. The results demonstrate that it is possible to reduce emotional information in the face representation while retaining competitive performance in other face-based artificial intelligence tasks.

Audio-Video Detection of the Active Speaker in Meetings

Francisco Madrigal, Frederic Lerasle, Lionel Pibre, Isabelle Ferrané

Auto-TLDR; Active Speaker Detection with Visual and Contextual Information from Meeting Context

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Meetings are a common activity that provides certain challenges when creating systems that assist them. Such is the case of the Active speaker detection, which can provide useful information for human interaction modeling, or human-robot interaction. Active speaker detection is mostly done using speech, however, certain visual and contextual information can provide additional insights. In this paper we propose an active speaker detection framework that integrates audiovisual features with social information, from the meeting context. Visual cue is processed using a Convolutional Neural Network (CNN) that captures the spatio-temporal relationships. We analyze several CNN architectures with both cues: raw pixels (RGB images) and motion (estimated with optical flow). Contextual reasoning is done with an original methodology, based on the gaze of all participants. We evaluate our proposal with a public \textcolor{black}{benchmark} in state-of-art: AMI corpus. We show how the addition of visual and context information improves the performance of the active speaker detection.

Two-Stream Temporal Convolutional Network for Dynamic Facial Attractiveness Prediction

Nina Weng, Jiahao Wang, Annan Li, Yunhong Wang

Auto-TLDR; 2S-TCN: A Two-Stream Temporal Convolutional Network for Dynamic Facial Attractiveness Prediction

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In the field of facial attractiveness prediction, while deep models using static pictures have shown promising results, little attention is paid to dynamic facial information, which is proven to be influential by psychological studies. Meanwhile, the increasing popularity of short video apps creates an enormous demand of facial attractiveness prediction from short video clips. In this paper, we target on the dynamic facial attractiveness prediction problem. To begin with, a large-scale video-based facial attractiveness prediction dataset (VFAP) with more than one thousand clips from TikTok is collected. A two-stream temporal convolutional network (2S-TCN) is then proposed to capture dynamic attractiveness feature from both facial appearance and landmarks. We employ attentive feature enhancement along with specially designed modality and temporal fusion strategies to better explore the temporal dynamics. Extensive experiments on the proposed VFAP dataset demonstrate that 2S-TCN has a distinct advantage over the state-of-the-art static prediction methods.

MRP-Net: A Light Multiple Region Perception Neural Network for Multi-Label AU Detection

Yang Tang, Shuang Chen, Honggang Zhang, Gang Wang, Rui Yang

Auto-TLDR; MRP-Net: A Fast and Light Neural Network for Facial Action Unit Detection

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Facial Action Units (AUs) are of great significance in communication. Automatic AU detection can improve the understanding of psychological condition and emotional status. Recently, a number of deep learning methods have been proposed to take charge with problems in automatic AU detection. Several challenges, like unbalanced labels and ignorance of local information, remain to be addressed. In this paper, we propose a fast and light neural network called MRP-Net, which is an end-to-end trainable method for facial AU detection to solve these problems. First, we design a Multiple Region Perception (MRP) module aimed at capturing different locations and sizes of features in the deeper level of the network without facial landmark points. Then, in order to balance the positive and negative samples in the large dataset, a batch balanced method adjusting the weight of every sample in one batch in our loss function is suggested. Experimental results on two popular AU datasets, BP4D and DISFA prove that MRP-Net outperforms state-of-the-art methods. Compared with the best method, not only does MRP-Net have an average F1 score improvement of 2.95% on BP4D and 5.43% on DISFA, and it also decreases the number of network parameters by 54.62% and the number of network FLOPs by 19.6%.

Attentive Hybrid Feature Based a Two-Step Fusion for Facial Expression Recognition

Jun Weng, Yang Yang, Zichang Tan, Zhen Lei

Auto-TLDR; Attentive Hybrid Architecture for Facial Expression Recognition

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Facial expression recognition is inherently a challenging task, especially for the in-the-wild images with various occlusions and large pose variations, which may lead to the loss of some crucial information. To address it, in this paper, we propose an attentive hybrid architecture (AHA) which learns global, local and integrated features based on different face regions. Compared with one type of feature, our extracted features own complementary information and can reduce the loss of crucial information. Specifically, AHA contains three branches, where all sub-networks in those branches employ the attention mechanism to further localize the interested pixels/regions. Moreover, we propose a two-step fusion strategy based on LSTM to deeply explore the hidden correlations among different face regions. Extensive experiments on four popular expression databases (i.e., CK+, FER-2013, SFEW 2.0, RAF-DB) show the effectiveness of the proposed method.

RWF-2000: An Open Large Scale Video Database for Violence Detection

Ming Cheng, Kunjing Cai, Ming Li

Auto-TLDR; Flow Gated Network for Violence Detection in Surveillance Cameras

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In recent years, surveillance cameras are widely deployed in public places, and the general crime rate has been reduced significantly due to these ubiquitous devices. Usually, these cameras provide cues and evidence after crimes were conducted, while they are rarely used to prevent or stop criminal activities in time. It is both time and labor consuming to manually monitor a large amount of video data from surveillance cameras. Therefore, automatically recognizing violent behaviors from video signals becomes essential. In this paper, we summarize several existing video datasets for violence detection and propose a new video dataset with 2,000 videos all captured by surveillance cameras in real-world scenes. Also, we present a new method that utilizes both the merits of 3D-CNNs and optical flow, namely Flow Gated Network. The proposed approach obtains an accuracy of 87.25% on the test set of our proposed RWF-2000 database. The proposed database and source codes of this paper are currently open to access.

SAT-Net: Self-Attention and Temporal Fusion for Facial Action Unit Detection

Zhihua Li, Zheng Zhang, Lijun Yin

Auto-TLDR; Temporal Fusion and Self-Attention Network for Facial Action Unit Detection

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Research on facial action unit detection has shown remarkable performances by using deep spatial learning models in recent years, however, it is far from reaching its full capacity in learning due to the lack of use of temporal information of AUs across time. Since the AU occurrence in one frame is highly likely related to previous frames in a temporal sequence, exploring temporal correlation of AUs across frames becomes a key motivation of this work. In this paper, we propose a novel temporal fusion and AU-supervised self-attention network (a so-called SAT-Net) to address the AU detection problem. First of all, we input the deep features of a sequence into a convolutional LSTM network and fuse the previous temporal information into the feature map of the last frame, and continue to learn the AU occurrence. Second, considering the AU detection problem is a multi-label classification problem that individual label depends only on certain facial areas, we propose a new self-learned attention mask by focusing the detection of each AU on parts of facial areas through the learning of individual attention mask for each AU, thus increasing the AU independence without the loss of any spatial relations. Our extensive experiments show that the proposed framework achieves better results of AU detection over the state-of-the-arts on two benchmark databases (BP4D and DISFA).

Vision-Based Multi-Modal Framework for Action Recognition

Djamila Romaissa Beddiar, Mourad Oussalah, Brahim Nini

Auto-TLDR; Multi-modal Framework for Human Activity Recognition Using RGB, Depth and Skeleton Data

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Human activity recognition plays a central role in the development of intelligent systems for video surveillance, public security, health care and home monitoring, where detection and recognition of activities can improve the quality of life and security of humans. Typically, automated, intuitive and real-time systems are required to recognize human activities and identify accurately unusual behaviors in order to prevent dangerous situations. In this work, we explore the combination of three modalities (RGB, depth and skeleton data) to design a robust multi-modal framework for vision-based human activity recognition. Especially, spatial information, body shape/posture and temporal evolution of actions are highlighted using illustrative representations obtained from a combination of dynamic RGB images, dynamic depth images and skeleton data representations. Therefore, each video is represented with three images that summarize the ongoing action. Our framework takes advantage of transfer learning from pre trained models to extract significant features from these newly created images. Next, we fuse extracted features using Canonical Correlation Analysis and train a Long Short-Term Memory network to classify actions from visual descriptive images. Experimental results demonstrated the reliability of our feature-fusion framework that allows us to capture highly significant features and enables us to achieve the state-of-the-art performance on the public UTD-MHAD and NTU RGB+D datasets.

IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition

Gibran Benitez-Garcia, Jesus Olivares-Mercado, Gabriel Sanchez-Perez, Keiji Yanai

Auto-TLDR; IPN Hand: A Benchmark Dataset for Continuous Hand Gesture Recognition

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Continuous hand gesture recognition (HGR) is an essential part of human-computer interaction with a wide range of applications in the automotive sector, consumer electronics, home automation, and others. In recent years, accurate and efficient deep learning models have been proposed for HGR. However, in the research community, the current publicly available datasets lack real-world elements needed to build responsive and efficient HGR systems. In this paper, we introduce a new benchmark dataset named IPN Hand with sufficient size, variation, and real-world elements able to train and evaluate deep neural networks. This dataset contains more than 4 000 gesture samples and 800 000 RGB frames from 50 distinct subjects. We design 13 different static and dynamic gestures focused on interaction with touchless screens. We especially consider the scenario when continuous gestures are performed without transition states, and when subjects perform natural movements with their hands as non-gesture actions. Gestures were collected from about 30 diverse scenes, with real-world variation in background and illumination. With our dataset, the performance of three 3D-CNN models is evaluated on the tasks of isolated and continuous real-time HGR. Furthermore, we analyze the possibility of increasing the recognition accuracy by adding multiple modalities derived from RGB frames, i.e., optical flow and semantic segmentation, while keeping the real-time performance of the 3D-CNN model. Our empirical study also provides a comparison with the publicly available nvGesture (NVIDIA) dataset. The experimental results show that the state-of-the-art ResNext-101 model decreases about 30% accuracy when using our real-world dataset, demonstrating that the IPN Hand dataset can be used as a benchmark, and may help the community to step forward in the continuous HGR.

Deep Multi-Task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing

Rui Zhao, Tianshan Liu, Jun Xiao, P. K. Daniel Lun, Kin-Man Lam

Auto-TLDR; Multi-task Learning for Facial Expression Recognition and Synthesis

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Multi-task learning is an effective learning strategy for deep-learning-based facial expression recognition tasks. However, most existing methods take into limited consideration the feature selection, when transferring information between different tasks, which may lead to task interference when training the multi-task networks. To address this problem, we propose a novel selective feature-sharing method, and establish a multi-task network for facial expression recognition and facial expression synthesis. The proposed method can effectively transfer beneficial features between different tasks, while filtering out useless and harmful information. Moreover, we employ the facial expression synthesis task to enlarge and balance the training dataset to further enhance the generalization ability of the proposed method. Experimental results show that the proposed method achieves state-of-the-art performance on those commonly used facial expression recognition benchmarks, which makes it a potential solution to real-world facial expression recognition problems.

Attribute-Based Quality Assessment for Demographic Estimation in Face Videos

Fabiola Becerra-Riera, Annette Morales-González, Heydi Mendez-Vazquez, Jean-Luc Dugelay

Auto-TLDR; Facial Demographic Estimation in Video Scenarios Using Quality Assessment

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Most existing works regarding facial demographic estimation are focused on still image datasets, although nowadays the need to analyze video content in real applications is increasing. We propose to tackle gender, age and ethnicity estimation in the context of video scenarios. Our main contribution is to use an attribute-specific quality assessment procedure to select best quality frames from a video sequence for each of the three demographic modalities. Best quality frames are classified with fine-tuned MobileNet models and a final video prediction is obtained with a majority voting strategy among the best selected frames. Our validation on three different datasets and our comparison with state-of-the-art models, show the effectiveness of the proposed demographic classifiers and the quality pipeline, which allows to reduce both: the number of frames to be classified and the processing time in practical applications; and improves the soft biometrics prediction accuracy.

Real-Time Driver Drowsiness Detection Using Facial Action Units

Malaika Vijay, Nandagopal Netrakanti Vinayak, Maanvi Nunna, Subramanyam Natarajan

Auto-TLDR; Real-Time Detection of Driver Drowsiness using Facial Action Units using Extreme Gradient Boosting

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This paper presents a two-stage, vision-based pipeline for the real-time detection of driver drowsiness using Facial Action Units (FAUs). FAUs capture movements in groups of muscles in the face like widening of the eyes or dropping of the jaw. The first stage of the pipeline employs a Convolutional Neural Network (CNN) trained to detect FAUs. The output of the penultimate layer of this network serves as an image embedding that captures features relevant to FAU detection. These embeddings are then used to predict drowsiness using an Extreme Gradient Boosting (XGBoost) classifier. A separate XGBoost model is trained for each user of the system so that behavior specific to each user can be modeled into the drowsiness classifier. We show that user-specific classifiers require very little data and low training time to yield high prediction accuracies in real-time.

Unconstrained Facial Expression Recogniton Based on Cascade Decision and Gabor Filters

Yanhong Wu, Lijie Zhang, Guannan Chen, Pablo Navarrete Michelini

Auto-TLDR; Convolutional Neural Network for Facial Expression Recognition under unconstrained natural conditions

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Facial Expression Recognition (FER) research with Convolutional Neural Networks (CNN) has been active, especially under unconstrained natural conditions. From our observation, prior arts treat expressions equally in classification and the reconition accuracy of some expression are always higher than others. In this paper, we make the assumption that an expression with a higher accuracy is easier to be recognized, and those expressions easier to recognize will hinder the recognition of uneasy expressions. Then, we propose a novel algorithm for unconstrained FER based on cascade decision and Gabor filters. Easier expressions are recognized before the difficult expressions. This simple method trains up to five models to cascadedly recognize a given facial image expression. The first binary classifier model is for the classification of Happy with the highest accuracy. The second binary classifier model is for the classification of Surprise with the second high accuracy. The third binary classifier model is for the classification of Neutral with the third high accuracy. The forth model is for the classification of Sad with the forth high accuracy. And the final model is 3-class classifier for Angry, Disgust and Fear. Gabor filters are included in every model to enhance robustness on illumination variations and face poses. Extensive experiment results on several datasets validate the effectiveness of the proposed method. We obtain accuracy of 77.6% on FER2013 with the final models, outperforming the latest state-of-the-arts.

Quantified Facial Temporal-Expressiveness Dynamics for Affect Analysis

Md Taufeeq Uddin, Shaun Canavan

Auto-TLDR; quantified facial Temporal-expressiveness Dynamics for quantified affect analysis

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The quantification of visual affect data (e.g. face images) is essential to build and monitor automated affect modeling systems efficiently. Considering this, this work proposes quantified facial Temporal-expressiveness Dynamics (TED) to quantify the expressiveness of human faces. The proposed algorithm leverages multimodal facial features by incorporating static and dynamic information to enable accurate measurements of facial expressiveness. We show that TED can be used for high-level tasks such as summarization of unstructured visual data, expectation from and interpretation of automated affect recognition models. To evaluate the positive impact of using TED, a case study was conducted on spontaneous pain using the UNBC-McMaster spontaneous shoulder pain dataset. Experimental results show the efficacy of using TED for quantified affect analysis.

Weight Estimation from an RGB-D Camera in Top-View Configuration

Marco Mameli, Marina Paolanti, Nicola Conci, Filippo Tessaro, Emanuele Frontoni, Primo Zingaretti

Auto-TLDR; Top-View Weight Estimation using Deep Neural Networks

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The development of so-called soft-biometrics aims at providing information related to the physical and behavioural characteristics of a person. This paper focuses on bodyweight estimation based on the observation from a top-view RGB-D camera. In fact, the capability to estimate the weight of a person can be of help in many different applications, from health-related scenarios to business intelligence and retail analytics. To deal with this issue, a TVWE (Top-View Weight Estimation) framework is proposed with the aim of predicting the weight. The approach relies on the adoption of Deep Neural Networks (DNNs) that have been trained on depth data. Each network has also been modified in its top section to replace classification with prediction inference. The performance of five state-of-art DNNs has been compared, namely VGG16, ResNet, Inception, DenseNet and Efficient-Net. In addition, a convolutional auto-encoder has also been included for completeness. Considering the limited literature in this domain, the TVWE framework has been evaluated on a new publicly available dataset: “VRAI Weight estimation Dataset”, which also collects, for each subject, labels related to weight, gender, and height. The experimental results have demonstrated that the proposed methods are suitable for this task, bringing different and significant insights for the application of the solution in different domains.

Face Anti-Spoofing Based on Dynamic Color Texture Analysis Using Local Directional Number Pattern

Junwei Zhou, Ke Shu, Peng Liu, Jianwen Xiang, Shengwu Xiong

Auto-TLDR; LDN-TOP Representation followed by ProCRC Classification for Face Anti-Spoofing

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Face anti-spoofing is becoming increasingly indispensable for face recognition systems, which are vulnerable to various spoofing attacks performed using fake photos and videos. In this paper, a novel "LDN-TOP representation followed by ProCRC classification" pipeline for face anti-spoofing is proposed. We use local directional number pattern (LDN) with the derivative-Gaussian mask to capture detailed appearance information resisting illumination variations and noises, which can influence the texture pattern distribution. To further capture motion information, we extend LDN to a spatial-temporal variant named local directional number pattern from three orthogonal planes (LDN-TOP). The multi-scale LDN-TOP capturing complete information is extracted from color images to generate the feature vector with powerful representation capacity. Finally, the feature vector is fed into the probabilistic collaborative representation based classifier (ProCRC) for face anti-spoofing. Our method is evaluated on three challenging public datasets, namely CASIA FASD, Replay-Attack database, and UVAD database using sequence-based evaluation protocol. The experimental results show that our method can achieve promising performance with 0.37% EER on CASIA and 5.73% HTER on UVAD. The performance on Replay-Attack database is also competitive.

Facial Expression Recognition by Using a Disentangled Identity-Invariant Expression Representation

Kamran Ali, Charles Hughes

Auto-TLDR; Transfer-based Expression Recognition Generative Adversarial Network (TER-GAN)

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Facial Expression Recognition (FER) is a challenging task because many factors of variation such as pose, illumination, and identity-specific attributes are entangled with the expression information in an expressive face image. Recent works show that the performance of a FER algorithm can be improved by disentangling the expression information from identity features. In this paper, we present Transfer-based Expression Recognition Generative Adversarial Network (TER-GAN) that combines the effectiveness of a novel feature disentanglement technique with the concept of identity-invariant expression representation learning for facial expression recognition. More specifically, TER-GAN learns a disentangled expression representation by extracting expression features from one image and transferring the expression information to the identity of another image. To improve the feature disentanglement process, and to learn an identity-invariant expression representation, we introduce a novel expression consistency loss and an identity consistency loss that exploit expression and identity information from both real and synthetic images. We evaluated the performance of our proposed facial expression recognition technique by employing five public facial expression databases, CK+, Oulu-CASIA, MMI, BU-3DFE, and BU-4DFE, the latter being used for pre-training. The experimental results show the effectiveness of the proposed technique.

Quality-Based Representation for Unconstrained Face Recognition

Nelson Méndez-Llanes, Katy Castillo-Rosado, Heydi Mendez-Vazquez, Massimo Tistarelli

Auto-TLDR; activation map for face recognition in unconstrained environments

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Significant advances have been achieved in face recognition in the last decade thanks to the development of deep learning methods. However, recognizing faces captured in uncontrolled environments is still a challenging problem for the scientific community. In these scenarios, the performance of most of existing deep learning based methods abruptly falls, due to the bad quality of the face images. In this work, we propose to use an activation map to represent the quality information in a face image. Different face regions are analyzed to determine their quality and then only those regions with good quality are used to perform the recognition using a given deep face model. For experimental evaluation, in order to simulate unconstrained environments, three challenging databases, with different variations in appearance, were selected: the Labeled Faces in the Wild Database, the Celebrities in Frontal-Profile in the Wild Database, and the AR Database. Three deep face models were used to evaluate the proposal on these databases and in all cases, the use of the proposed activation map allows the improvement of the recognition rates obtained by the original models in a range from 0.3 up to 31%. The obtained results experimentally demonstrated that the proposal is able to select those face areas with higher discriminative power and enough identifying information, while ignores the ones with spurious information.

Learning Visual Voice Activity Detection with an Automatically Annotated Dataset

Stéphane Lathuiliere, Pablo Mesejo, Radu Horaud

Auto-TLDR; Deep Visual Voice Activity Detection with Optical Flow

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Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not. V-VAD is useful whenever audio VAD (A-VAD) is inefficient either because the acoustic signal is difficult to analyze or is simply missing. We propose two deep architectures for V-VAD, one based on facial landmarks and one based on optical flow. Moreover, available datasets, used for learning and for testing V-VAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets in-the-wild, based on combining A-VAD and face detection. A thorough empirical evaluation shows the advantage of training the proposed deep V-VAD models with such a dataset.

Self-Supervised Learning of Dynamic Representations for Static Images

Siyang Song, Enrique Sanchez, Linlin Shen, Michel Valstar

Auto-TLDR; Facial Action Unit Intensity Estimation and Affect Estimation from Still Images with Multiple Temporal Scale

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Facial actions are spatio-temporal signals by nature, and therefore their modeling is crucially dependent on the availability of temporal information. In this paper, we focus on inferring such temporal dynamics of facial actions when no explicit temporal information is available, i.e. from still images. We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation. In particular, 1) we propose a framework that infers a dynamic representation (DR) from a still image, which captures the bi-directional flow of time within a short time-window centered at the input image; 2) we show that we can train our method without the need of explicitly generating target representations, allowing the network to represent dynamics more broadly; and 3) we propose to apply a multiple temporal scale approach that infers DRs for different window lengths (MDR) from a still image. We empirically validate the value of our approach on the task of frame ranking, and show how our proposed MDR attains state of the art results on BP4D for AU intensity estimation and on SEMAINE for dimensional affect estimation, using only still images at test time.

Detecting Anomalies from Video-Sequences: A Novel Descriptor

Giulia Orrù, Davide Ghiani, Maura Pintor, Gian Luca Marcialis, Fabio Roli

Auto-TLDR; Trit-based Measurement of Group Dynamics for Crowd Behavior Analysis and Anomaly Detection

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We present a novel descriptor for crowd behavior analysis and anomaly detection. The goal is to measure by appropriate patterns the speed of formation and disintegration of groups in the crowd. This descriptor is inspired by the concept of one-dimensional local binary patterns: in our case, such patterns depend on the number of group observed in a time window. An appropriate measurement unit, named "trit" (trinary digit), represents three possible dynamic states of groups on a certain frame. Our hypothesis is that abrupt variations of the groups' number may be due to an anomalous event that can be accordingly detected, by translating these variations on temporal trit-based sequence of strings which are significantly different from the one describing the "no-anomaly" one. Due to the peculiarity of the rationale behind this work, relying on the number of groups, three different methods of people group's extraction are compared. Experiments are carried out on the Motion-Emotion benchmark data set. Reported results point out in which cases the trit-based measurement of group dynamics allows us to detect the anomaly. Besides the promising performance of our approach, we show how it is correlated with the anomaly typology and the camera's perspective to the crowd's flow (frontal, lateral).

Late Fusion of Bayesian and Convolutional Models for Action Recognition

Camille Maurice, Francisco Madrigal, Frederic Lerasle

Auto-TLDR; Fusion of Deep Neural Network and Bayesian-based Approach for Temporal Action Recognition

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The activities we do in our daily-life are generally carried out as a succession of atomic actions, following a logical order. During a video sequence, actions usually follow a logical order. In this paper, we propose a hybrid approach resulting from the fusion of a deep learning neural network with a Bayesian-based approach. The latter models human-object interactions and transition between actions. The key idea is to combine both approaches in the final prediction. We validate our strategy in two public datasets: CAD-120 and Watch-n-Patch. We show that our fusion approach yields performance gains in accuracy of respectively +4\% and +6\% over a baseline approach. Temporal action recognition performances are clearly improved by the fusion, especially when classes are imbalanced.

Early Wildfire Smoke Detection in Videos

Taanya Gupta, Hengyue Liu, Bir Bhanu

Auto-TLDR; Semi-supervised Spatio-Temporal Video Object Segmentation for Automatic Detection of Smoke in Videos during Forest Fire

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Recent advances in unmanned aerial vehicles and camera technology have proven useful for the detection of smoke that emerges above the trees during a forest fire. Automatic detection of smoke in videos is of great interest to Fire department. To date, in most parts of the world, the fire is not detected in its early stage and generally it turns catastrophic. This paper introduces a novel technique that integrates spatial and temporal features in a deep learning framework using semi-supervised spatio-temporal video object segmentation and dense optical flow. However, detecting this smoke in the presence of haze and without the labeled data is difficult. Considering the visibility of haze in the sky, a dark channel pre-processing method is used that reduces the amount of haze in video frames and consequently improves the detection results. Online training is performed on a video at the time of testing that reduces the need for ground-truth data. Tests using the publicly available video datasets show that the proposed algorithms outperform previous work and they are robust across different wildfire-threatened locations.

Temporal Binary Representation for Event-Based Action Recognition

Simone Undri Innocenti, Federico Becattini, Federico Pernici, Alberto Del Bimbo

Auto-TLDR; Temporal Binary Representation for Gesture Recognition

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In this paper we present an event aggregation strategy to convert the output of an event camera into frames processable by traditional Computer Vision algorithms. The proposed method first generates sequences of intermediate binary representations, which are then losslessly transformed into a compact format by simply applying a binary-to-decimal conversion. This strategy allows us to encode temporal information directly into pixel values, which are then interpreted by deep learning models. We apply our strategy, called Temporal Binary Representation, to the task of Gesture Recognition, obtaining state of the art results on the popular DVS128 Gesture Dataset. To underline the effectiveness of the proposed method compared to existing ones, we also collect an extension of the dataset under more challenging conditions on which to perform experiments.

Detecting Manipulated Facial Videos: A Time Series Solution

Zhang Zhewei, Ma Can, Gao Meilin, Ding Bowen

Auto-TLDR; Face-Alignment Based Bi-LSTM for Fake Video Detection

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We propose a new method to expose fake videos based on a time series solution. The method is based on bidirectional long short-term memory (Bi-LSTM) backbone architecture with two different types of features: {Face-Alignment} and {Dense-Face-Alignment}, in which both of them are physiological signals that can be distinguished between fake and original videos. We choose 68 landmark points as the feature of {Face-Alignment} and Pose Adaptive Feature (PAF) for {Dense-Face-Alignment}. Based on these two facial features, we designed two deep networks. In addition, we optimize our network by adding an attention mechanism that improves detection precision. Our method is tested over benchmarks of Face Forensics/Face Forensics++ dataset and show a promising performance on inference speed while maintaining accuracy with state-of art solutions that deal against DeepFake.

Electroencephalography Signal Processing Based on Textural Features for Monitoring the Driver’s State by a Brain-Computer Interface

Giulia Orrù, Marco Micheletto, Fabio Terranova, Gian Luca Marcialis

Auto-TLDR; One-dimensional Local Binary Pattern Algorithm for Estimating Driver Vigilance in a Brain-Computer Interface System

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In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of the solution proposed relies on employing the one-dimensional Local Binary Pattern (1D-LBP) algorithm for feature extraction from pre-processed EEG data. From the resulting feature vector, the classification is done according to three vigilance classes: awake, tired and drowsy. The claim is that the class transitions can be detected by describing the variations of the micro-patterns' occurrences along the EEG signal. The 1D-LBP is able to describe them by detecting mutual variations of the signal temporarily "close" as a short bit-code. Our analysis allows to conclude that the 1D-LBP adoption has led to significant performance improvement. Moreover, capturing the class transitions from the EEG signal is effective, although the overall performance is not yet good enough to develop a BCI for assessing the driver's vigilance in real environments.

Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution

Stefan Zernetsch, Steven Schreck, Viktor Kress, Konrad Doll, Bernhard Sick

Auto-TLDR; 3D-ConvNet: A Multi-stream 3D Convolutional Neural Network for Detecting Cyclists in Real World Traffic Situations

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In this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture. To resolve occlusions of cyclists by other traffic participants or road structures, we use a wide angle stereo camera system mounted at a heavily frequented public intersection. We created a large dataset consisting of 1,639 video sequences containing cyclists, recorded in real world traffic, resulting in over 1.1 million samples. Through modeling the cyclists' behavior by a state machine of basic cyclist movements, our approach takes every situation into account and is not limited to certain scenarios. We compare our method to an approach solely based on position sequences. Both methods are evaluated taking into account frame wise and scene wise classification results of basic movements, and detection times of basic movement transitions, where our approach outperforms the position based approach by producing more reliable detections with shorter detection times. Our code and parts of our dataset are made publicly available.

Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition

Jianrong Wang, Tong Wu, Shanyu Wang, Mei Yu, Qiang Fang, Ju Zhang, Li Liu

Auto-TLDR; Lip Motion Network for Text-Independent and Text-Dependent Speaker Recognition

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Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two dimensional (2D) lip images to recognize speaker in a text-dependent context. However, 2D lip easily suffers from face orientations. To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent contexts. A novel regional feedback module (RFM) is proposed to explore attentions in different lip regions. Besides, prior knowledge of lip motion is investigated to complement RFM, where landmark-level and frame-level features are merged to form a better feature representation. Moreover, we present two methods, i.e., coordinate transformation and face posture correction to pre-process the LSD-AV dataset, which contains 68 speakers and 146 sentences per speaker. The evaluation results on this dataset demonstrate that our proposed 3LMNet is superior to the baseline models, i.e., LSTM, VGG-16 and ResNet-34, and outperforms the state-of-the-art using 2D lip image as well as the 3D face. The code of this work is released at https://github.com/wutong18/Three-Dimensional-Lip-Motion-Ne twork-for-Text-Independent-Speaker-Recognition.

Face Anti-Spoofing Using Spatial Pyramid Pooling

Lei Shi, Zhuo Zhou, Zhenhua Guo

Auto-TLDR; Spatial Pyramid Pooling for Face Anti-Spoofing

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Face recognition system is vulnerable to many kinds of presentation attacks, so how to effectively detect whether the image is from the real face is particularly important. At present, many deep learning-based anti-spoofing methods have been proposed. But these approaches have some limitations, for example, global average pooling (GAP) easily loses local information of faces, single-scale features easily ignore information differences in different scales, while a complex network is prune to be overfitting. In this paper, we propose a face anti-spoofing approach using spatial pyramid pooling (SPP). Firstly, we use ResNet-18 with a small amount of parameter as the basic model to avoid overfitting. Further, we use spatial pyramid pooling module in the single model to enhance local features while fusing multi-scale information. The effectiveness of the proposed method is evaluated on three databases, CASIA-FASD, Replay-Attack and CASIA-SURF. The experimental results show that the proposed approach can achieve state-of-the-art performance.

A Quantitative Evaluation Framework of Video De-Identification Methods

Sathya Bursic, Alessandro D'Amelio, Marco Granato, Giuliano Grossi, Raffaella Lanzarotti

Auto-TLDR; Face de-identification using photo-reality and facial expressions

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We live in an era of privacy concerns, motivating a large research effort in face de-identification. As in other fields, we are observing a general movement from hand-crafted methods to deep learning methods, mainly involving generative models. Although these methods produce more natural de-identified images or videos, we claim that the mere evaluation of the de-identification is not sufficient, especially when it comes to processing the images/videos further. In this note, we take into account the issue of preserving privacy, facial expressions, and photo-reality simultaneously, proposing a general testing framework. The method is applied to four open-source tools, producing a baseline for future de-identification methods.

Activity Recognition Using First-Person-View Cameras Based on Sparse Optical Flows

Peng-Yuan Kao, Yan-Jing Lei, Chia-Hao Chang, Chu-Song Chen, Ming-Sui Lee, Yi-Ping Hung

Auto-TLDR; 3D Convolutional Neural Network for Activity Recognition with FPV Videos

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First-person-view (FPV) cameras are finding wide use in daily life to record activities and sports. In this paper, we propose a succinct and robust 3D convolutional neural network (CNN) architecture accompanied with an ensemble-learning network for activity recognition with FPV videos. The proposed 3D CNN is trained on low-resolution (32x32) sparse optical flows using FPV video datasets consisting of daily activities. According to the experimental results, our network achieves an average accuracy of 90%.

Inner Eye Canthus Localization for Human Body Temperature Screening

Claudio Ferrari, Lorenzo Berlincioni, Marco Bertini, Alberto Del Bimbo

Auto-TLDR; Automatic Localization of the Inner Eye Canthus in Thermal Face Images using 3D Morphable Face Model

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In this paper, we propose an automatic approach for localizing the inner eye canthus in thermal face images. We first coarsely detect 5 facial keypoints corresponding to the center of the eyes, the nosetip and the ears. Then we compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model (3DMM). This correspondence is used to project the entire 3D face onto the image, and subsequently locate the inner eye canthus. Detecting this location allows to obtain the most precise body temperature measurement for a person using a thermal camera. We evaluated the approach on a thermal face dataset provided with manually annotated landmarks. However, such manual annotations are normally conceived to identify facial parts such as eyes, nose and mouth, and are not specifically tailored for localizing the eye canthus region. As additional contribution, we enrich the original dataset by using the annotated landmarks to deform and project the 3DMM onto the images. Then, by manually selecting a small region corresponding to the eye canthus, we enrich the dataset with additional annotations. By using the manual landmarks, we ensure the correctness of the 3DMM projection, which can be used as ground-truth for future evaluations. Moreover, we supply the dataset with the 3D head poses and per-point visibility masks for detecting self-occlusions. The data will be publicly released.

A Cross Domain Multi-Modal Dataset for Robust Face Anti-Spooﬁng

Qiaobin Ji, Shugong Xu, Xudong Chen, Shan Cao, Shunqing Zhang

Auto-TLDR; Cross domain multi-modal FAS dataset GREAT-FASD and several evaluation protocols for academic community

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Face Anti-spooﬁng (FAS) is a challenging problem due to the complex serving scenario and diverse face presentation attack patterns. Using single modal images which are usually captured with RGB cameras is not able to deal with the former because of serious overﬁtting problems. The existing multi-modal FAS datasets rarely pay attention to the cross domain problems, trainingFASsystemonthesedataleadstoinconsistenciesandlow generalization capabilities in deployment since imaging principles(structured light, TOF, etc.) and pre-processing methods vary between devices. We explore the subtle ﬁne-grained differences betweeen multi-modal cameras and proposed a cross domain multi-modal FAS dataset GREAT-FASD and several evaluation protocols for academic community. Furthermore, we incorporate the multiplicative attention and center loss to enhance the representative power of CNN via seeking out complementary information as a powerful baseline. In addition, extensive experiments have been conducted on the proposed dataset to analyze the robustness to distinguish spoof faces and bona-ﬁde faces. Experimental results show the effectiveness of proposed method and achieve the state-of-the-art competitive results. Finally, we visualize our future distribution in hidden space and observe that the proposed method is able to lead the network to generate a large margin for face anti-spooﬁng task

Applying (3+2+1)D Residual Neural Network with Frame Selection for Hong Kong Sign Language Recognition

Zhenxing Zhou, King-Shan Lui, Vincent W.L. Tam, Edmund Y. Lam

Auto-TLDR; Hong Kong Sign Language Recognition with 3D Residual Neural Network and Resilience Model

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As reported by Hong Kong Government in 2017, there are more than 1.5 million residents suffering from hearing impairment in Hong Kong. Most of them rely on Hong Kong Sign Language for daily communication while there are only 63 registered sign language interpreters in Hong Kong. To address this specific social issue and also facilitate the effective communication between the hearing impaired and other people, this paper introduces a word-level Hong Kong Sign Language(HKSL) dataset which currently includes 45 isolated words and at least 30 sign videos per word performed by different signers(more than 1500 videos in total now and still enlarging). Based on this dataset, this paper systemically compares the performances of various deep learning approaches, including (1) 2D histogram of oriented gradients(HOG) feature/pose estimation/feature extraction with long-short term memory(LSTM) layer; (2) 3D Residual Neural Network(ResNet) (3) (2+1)D Residual Neural Network, in HKSL recognition. Meanwhile, to further improve the accuracy of sign language recognition, this paper proposes a novel method called (3+2+1)D ResNet Model with Frame Selection which adopts blurriness detection with Laplacian kernel to construct highquality video clips and also combines both (2+1)D and 3D ResNet for recognizing the sign language. At the end, the experimental results show that the proposed method outperforms other deep learning approaches and attain an impressive accuracy of 94.6% in our dataset.

A Grid-Based Representation for Human Action Recognition

Soufiane Lamghari, Guillaume-Alexandre Bilodeau, Nicolas Saunier

Auto-TLDR; GRAR: Grid-based Representation for Action Recognition in Videos

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Human action recognition (HAR) in videos is a fundamental research topic in computer vision. It consists mainly in understanding actions performed by humans based on a sequence of visual observations. In recent years, HAR have witnessed significant progress, especially with the emergence of deep learning models. However, most of existing approaches for action recognition rely on information that is not always relevant for the task, and are limited in the way they fuse temporal information. In this paper, we propose a novel method for human action recognition that encodes efficiently the most discriminative appearance information of an action with explicit attention on representative pose features, into a new compact grid representation. Our GRAR (Grid-based Representation for Action Recognition) method is tested on several benchmark datasets that demonstrate that our model can accurately recognize human actions, despite intra-class appearance variations and occlusion challenges.

AttendAffectNet: Self-Attention Based Networks for Predicting Affective Responses from Movies

Thi Phuong Thao Ha, Bt Balamurali, Herremans Dorien, Roig Gemma

Auto-TLDR; AttendAffectNet: A Self-Attention Based Network for Emotion Prediction from Movies

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In this work, we propose different variants of the self-attention based network for emotion prediction from movies, which we call AttendAffectNet. We take both audio and video into account and incorporate the relation among multiple modalities by applying self-attention mechanism in a novel manner into the extracted features for emotion prediction. We compare it to the typically temporal integration of the self-attention based model, which in our case, allows to capture the relation of temporal representations of the movie while considering the sequential dependencies of emotion responses. We demonstrate the effectiveness of our proposed architectures on the extended COGNIMUSE dataset [1], [2] and the MediaEval 2016 Emotional Impact of Movies Task [3], which consist of movies with emotion annotations. Our results show that applying the self-attention mechanism on the different audio-visual features, rather than in the time domain, is more effective for emotion prediction. Our approach is also proven to outperform state-of-the-art models for emotion prediction.

Spatial Bias in Vision-Based Voice Activity Detection

Kalin Stefanov, Mohammad Adiban, Giampiero Salvi

Auto-TLDR; Spatial Bias in Vision-based Voice Activity Detection in Multiparty Human-Human Interactions

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We present models for automatic vision-based voice activity detection (VAD) in multiparty human-human interactions that are aimed at complementing the acoustic VAD methods. We provide evidence that this type of vision-based VAD models are susceptible to spatial bias in the datasets. The physical settings of the interaction, usually constant throughout data acquisition, determines the distribution of head poses of the participants. Our results show that when the head pose distributions are significantly different in the training and test sets, the performance of the models drops significantly. This suggests that previously reported results on datasets with a fixed physical configuration may overestimate the generalization capabilities of this type of models. We also propose a number of possible remedies to the spatial bias, including data augmentation, input masking and dynamic features, and provide an in-depth analysis of the visual cues used by our models.