This report is about facial asymmetry, its connection to emotional expression, and methods of measuring facial asymmetry in videos of faces. The research was motivated by two factors: firstly, there was a real opportunity to develop a novel measure of asymmetry that required minimal human involvement and that improved on earlier measures in the literature; and secondly, the study of the relationship between facial asymmetry and emotional expression is both interesting in its own right, and important because it can inform neuropsychological theory and answer open questions concerning emotional processing in the brain. The two aims of the research were: first, to develop an automatic frame-by-frame measure of facial asymmetry in videos of faces that improved on previous measures; and second, to use the measure to analyse the relationship between facial asymmetry and emotional expression, and connect our findings with previous research of the relationship.
EmoDiffusion: Enhancing Emotional 3D Facial Animation with Latent Diffusion Models
University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology Beijing（北京科技大学）；The New Laboratory of Pattern Recognition (NLPR), State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室模式识别新实验室）；The New Laboratory of Pattern Recognition (NLPR)（模式识别新实验室）
Friedrich Schiller University of Jena（耶拿弗里德里希·席勒大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of political interactions, or interpersonal conflict behavior. Facial expressions are widely described as being automatic and thus hard to overtly influence. As such, they are a perfect measure for a better understanding of unintentional behavior cues about social-emotional cognitive processes. With this view, this study is concerned with the analysis of the direction of emotional influence in dyadic dialogue based on facial …
Second University of Naples（那不勒斯第二大学）；IIASS；Brno University of Technology（布尔诺理工大学）；EUP Mataró（马塔罗EUP）
This work defines a procedure for collecting naturally induced emotional facial expressions through the vision of movie excerpts with high emotional contents and reports experimental data ascertaining the effects of emotions on memory word recognition tasks. The induced emotional states include the four basic emotions of sadness, disgust, happiness, and surprise, as well as the neutral emotional state. The resulting database contains both thermal and visible emotional facial expressions, portrayed by forty Italian subjects and simultaneously acquired by appropriately synchronizing a thermal an…
Knowledge-Enhanced Facial Expression Recognition with Emotional-to-Neutral Transformation
Hong Kong Baptist University（香港浸会大学）；Xidian University（西安电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Open University（开放大学）；Weizmann Institute of Science（魏茨曼科学研究所）；University of Bern（伯尔尼大学）；University of Lincoln（林肯大学）；dogs and science - Institute for Canine Science and Applied Cynology（狗与科学——犬类科学与应用犬学研究所）；University of Haifa（海法大学）
Similarly to humans, facial expressions in animals are closely linked with emotional states. However, in contrast to the human domain, automated recognition of emotional states from facial expressions in animals is underexplored, mainly due to difficulties in data collection and establishment of ground truth concerning emotional states of non-verbal users. We apply recent deep learning techniques to classify (positive) anticipation and (negative) frustration of dogs on a dataset collected in a controlled experimental setting. We explore the suitability of different backbones (e.g. ResNet, ViT)…
There is little prior work on quantifying the relationships between facial expressions and emotionality in American Sign Language. In this final report, we provide two methods for studying these relationships through probability and prediction. Using a large corpus of natural signing manually annotated with facial features paired with lexical emotion datasets, we find that there exist many relationships between emotionality and the face, and that a simple classifier can predict what someone is saying in terms of broad emotional categories only by looking at the face.
Facebook AI Applied Research（Facebook人工智能应用研究）；University of Bristol（布里斯托大学）；Imperial College London（帝国理工学院）
Apparent emotional facial expression recognition has attracted a lot of research attention recently. However, the majority of approaches ignore age differences and train a generic model for all ages. In this work, we study the effect of using different age-groups for training apparent emotional facial expression recognition models. To this end, we study Domain Generalisation in the context of apparent emotional facial expression recognition from facial imagery across different age groups. We first compare several domain generalisation algorithms on the basis of out-of-domain-generalisation, an…
FEALLM: Advancing Facial Emotion Analysis in Multimodal Large Language Models with Emotional Synergy and Reasoning
Tianjin University（天津大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Lappeenranta-Lahti University of Technology LUT（拉佩兰塔-拉赫蒂技术大学）；Great Bay University（大湾大学）；Southeast University（东南大学）；Xi’an Jiaotong University（西安交通大学）
Polish-Japanese Academy of Information Technology（波日信息技术学院）；Tooploox（Tooploox公司）；Warsaw University of Technology（华沙理工大学）
Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we exte…
Xidian University（西安电子科技大学）；JD Finance America Corporation（京东金融美国公司）；Peking University（北京大学）
By utilizing label distribution learning, a probability distribution is assigned for a facial image to express a compound emotion, which effectively improves the problem of label uncertainties and noises occurred in one-hot labels. In practice, it is observed that correlations among emotions are inherently different, such as surprised and happy emotions are more possibly synchronized than surprised and neutral. It indicates the correlation may be crucial for obtaining a reliable label distribution. Based on this, we propose a new method that amends the label distribution of each facial image b…
Universidad Autonoma de Madrid（马德里自治大学）；Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；Massachusetts Institute of Technology（麻省理工学院）
We propose two face representations that are blind to facial expressions associated to emotional responses. This work is in part motivated by new international regulations for personal data protection, which enforce 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 triggers potential risks for humans, both in terms of fairness and privacy. We propose two different methods to learn these expres…
University of North Carolina at Greensboro（北卡罗来纳大学格林斯伯勒分校）
We present a Fourier-based machine learning technique that characterizes and detects facial emotions. The main challenging task in the development of machine learning (ML) models for classifying facial emotions is the detection of accurate emotional features from a set of training samples, and the generation of feature vectors for constructing a meaningful feature space and building ML models. In this paper, we hypothesis that the emotional features are hidden in the frequency domain; hence, they can be captured by leveraging the frequency domain and masking techniques. We also make use of the…
Rochester Institute of Technology（罗切斯特理工学院）；University of Rochester Medical Center（罗切斯特大学医学中心）
One-sided facial paralysis causes uneven movements of facial muscles on the sides of the face. Physicians currently assess facial asymmetry in a subjective manner based on their clinical experience. This paper proposes a novel method to provide an objective and quantitative asymmetry score for frontal faces. Our metric has the potential to help physicians for diagnosis as well as monitoring the rehabilitation of patients with one-sided facial paralysis. A deep learning based landmark detection technique is used to estimate style invariant facial landmark points and dense optical flow is used t…
Geometry-guided Emotion Modulation for Controllable and Photorealistic Emotional Talking Face Generation
School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）
When Words Smile: Generating Diverse Emotional Facial Expressions from Text
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；University of Macau（澳门大学）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）
Affectiva, a Smart Eye company（Affectiva（Smart Eye旗下公司））
In this paper we introduce AFFDEX 2.0 - a toolkit for analyzing facial expressions in the wild, that is, it is intended for users aiming to; a) estimate the 3D head pose, b) detect facial Action Units (AUs), c) recognize basic emotions and 2 new emotional states (sentimentality and confusion), and d) detect high-level expressive metrics like blink and attention. AFFDEX 2.0 models are mainly based on Deep Learning, and are trained using a large-scale naturalistic dataset consisting of thousands of participants from different demographic groups. AFFDEX 2.0 is an enhanced version of our previous …
Academy for Engineering & Technology, Fudan University（复旦大学工程与技术学院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Yiwu Research Institute, Fudan University（复旦大学义乌研究院）
Netease Fuxi AI Lab（网易伏羲人工智能实验室）；University of Technology Sydney（悉尼科技大学）
In this paper, we propose a novel text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions in accordance with contextual sentiments as well as speech rhythm and pauses. To be specific, our framework consists of a speaker-independent stage and a speaker-specific stage. In the speaker-independent stage, we design three parallel networks to generate animation parameters of the mouth, upper face, and head from texts, separately. In the speaker-specific stage, we present a 3D face model guided attention network to synthesize videos tailor…
Nanjing University（南京大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Sydney（悉尼大学）；SenseTime Research（商汤科技研究院）；Nanyang Technological University（南洋理工大学）；Tsinghua University（清华大学）
Despite previous success in generating audio-driven talking heads, most of the previous studies focus on the correlation between speech content and the mouth shape. Facial emotion, which is one of the most important features on natural human faces, is always neglected in their methods. In this work, we present Emotional Video Portraits (EVP), a system for synthesizing high-quality video portraits with vivid emotional dynamics driven by audios. Specifically, we propose the Cross-Reconstructed Emotion Disentanglement technique to decompose speech into two decoupled spaces, i.e., a duration-indep…
Charles Sturt University（查尔斯特大学）；Western Sydney University（西悉尼大学）；Kent Institute Australia（澳大利亚肯特学院）；Asia Pacific International College（亚太国际学院）；University of Kurdistan Hewler（库尔德斯坦休勒大学）；Al Iraqia University（伊拉克大学）
Facial emotional recognition is one of the essential tools used by recognition psychology to diagnose patients. Face and facial emotional recognition are areas where machine learning is excelling. Facial Emotion Recognition in an unconstrained environment is an open challenge for digital image processing due to different environments, such as lighting conditions, pose variation, yaw motion, and occlusions. Deep learning approaches have shown significant improvements in image recognition. However, accuracy and time still need improvements. This research aims to improve facial emotion recognitio…
Prior-based Objective Inference Mining Potential Uncertainty for Facial Expression Recognition
College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Frontiers Science Center for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统前沿科学中心）
Seoul National University of Science and Technology（首尔国立科技大学）
Facial expression in-the-wild is essential for various interactive computing domains. Especially, "Emotional Reaction Intensity" (ERI) is an important topic in the facial expression recognition task. In this paper, we propose a multi-emotional task learning-based approach and present preliminary results for the ERI challenge introduced in the 5th affective behavior analysis in-the-wild (ABAW) competition. Our method achieved the mean PCC score of 0.3254.
AU-Blendshape for Fine-grained Stylized 3D Facial Expression Manipulation
Beihang University（北京航空航天大学）；Peng Cheng Laboratory（鹏城实验室）；North China University of Technology（华北理工大学）；University of Washington（华盛顿大学）；University of Copenhagen（哥本哈根大学）
Nanjing University（南京大学）；The Chinese University of Hong Kong（香港中文大学）；University of Sydney（悉尼大学）；Monash University（莫纳什大学）
Although significant progress has been made to audio-driven talking face generation, existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In this paper, we propose the Emotion-Aware Motion Model (EAMM) to generate one-shot emotional talking faces by involving an emotion source video. Specifically, we first propose an Audio2Facial-Dynamics module, which renders talking faces from audio-driven unsupervised zero- and first-order key-points motion. Then through exploring the motion model's properties, we further propose an Implicit Emotion Displacement Learner…
Chongqing University（重庆大学）；University of Waterloo（滑铁卢大学）；Shenzhen University（深圳大学）
In this paper, we introduce a new dataset, the driver emotion facial expression (DEFE) dataset, for driver spontaneous emotions analysis. The dataset includes facial expression recordings from 60 participants during driving. After watching a selected video-audio clip to elicit a specific emotion, each participant completed the driving tasks in the same driving scenario and rated their emotional responses during the driving processes from the aspects of dimensional emotion and discrete emotion. We also conducted classification experiments to recognize the scales of arousal, valence, dominance, …
Macquarie University（麦考瑞大学）；The Australian National University（澳大利亚国立大学）
Image captioning is the process of generating a natural language description of an image. Most current image captioning models, however, do not take into account the emotional aspect of an image, which is very relevant to activities and interpersonal relationships represented therein. Towards developing a model that can produce human-like captions incorporating these, we use facial expression features extracted from images including human faces, with the aim of improving the descriptive ability of the model. In this work, we present two variants of our Face-Cap model, which embed facial expres…
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
Renmin University of China（中国人民大学）；Nanjing University of Science and Technology（南京理工大学）；The Hong Kong University of Science and Technology（香港科技大学）；Psyche AI Inc.（赛琪人工智能公司）；Chinese Academy of Sciences（中国科学院）；Tsinghua University（清华大学）
Australian National University（澳大利亚国立大学）；Sun Yat-sen University（中山大学）；DATA61, CSIRO（联邦科学与工业研究组织数据61所）
Human eyes concentrate different facial regions during distinct cognitive activities. We study utilising facial visual saliency maps to classify different facial expressions into different emotions. Our results show that our novel method of merely using facial saliency maps can achieve a descent accuracy of 65\%, much higher than the chance level of $1/7$. Furthermore, our approach is of semi-supervision, i.e., our facial saliency maps are generated from a general saliency prediction algorithm that is not explicitly designed for face images. We also discovered that the classification accuracie…
PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS部）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Psyche AI.INC（Psyche AI公司）；HKUST（香港科技大学）；CAIR, HKISI, Chinese Academy of Sciences（中国科学院计算智能研究所）；SCSE, FIE, M.U.S.T（M.U.S.T的SCSE、FIE部门）
LSF-Animation: Label-Free Speech-Driven Facial Animation via Implicit Feature Representation
University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Academy（中关村学院）；National University of Singapore（新加坡国立大学）；Chongqing University（重庆大学）；University of Science and Technology of China（中国科学技术大学）
