This paper proposes a feature-based domain adaptation technique for identifying emotions in generic images, encompassing both facial and non-facial objects, as well as non-human components. This approach addresses the challenge of the limited availability of pre-trained models and well-annotated datasets for Image Emotion Recognition (IER). Initially, a deep-learning-based Facial Expression Recognition (FER) system is developed, classifying facial images into discrete emotion classes. Maintaining the same network architecture, this FER system is then adapted to recognize emotions in generic images through the application of discrepancy loss, enabling the model to effectively learn IER features while classifying emotions into categories such as 'happy,' 'sad,' 'hate,' and 'anger.' Additionally, a novel interpretability method, Divide and Conquer based Shap (DnCShap), is introduced to elucidate the visual features most relevant for emotion recognition. The proposed IER system demonstrated emotion classification accuracies of 61.86% for the IAPSa dataset, 62.47 for the ArtPhoto dataset, 70.78% for the FI dataset, and 59.72% for the EMOTIC dataset. The system effectively identifies the important visual features that lead to specific emotion classifications and also provides detailed embedding plots explaining the predictions, enhancing the understanding and trust in AI-driven emotion recognition systems.
Pontifícia Universidade Católica do Paraná (PUCPR)（巴拉那天主教大学（PUCPR））；State University of Ponta Grossa (UEPG)（蓬塔格罗萨州立大学（UEPG））；École de Technologie Supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院（ÉTS））
This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The idea is to create complementary representations promoting diversity by varying the autoencoders' initialization, architecture, and training data. SVM, Bagging, Random Forest, and a dynamic ensemble selection method are evaluated as final classification methods. Experimental results on Jaffe and Cohn-Kanade datasets using a leave-one-subject-out protocol show that FER methods based on the proposed diverse representations…
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, …
Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over $3000$ facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with dif…
iCV Research Group, Institute of Technology, University of Tartu（塔尔图大学技术研究所 iCV 研究组）；Computer Vision Center（计算机视觉中心）；University of Barcelona（巴塞罗那大学）；University of Autonoma, Barcelona（巴塞罗那自治大学）；Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；Institute of Physiology and Pathology of Hearing（听力生理学与病理学研究所）；Department of Psychology, University of Tartu（塔尔图大学心理学系）；The Estonian Center of Behavioral and Health Sciences（爱沙尼亚行为与健康科学中心）；Department of Electrical and Electronic Engineering, Hasan Kalyoncu University（哈桑·卡尔永库大学电气与电子工程系）
Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are…
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）
University of Wisconsin – Eau Claire（威斯康星大学欧克莱尔分校）
Facial emotion recognition is a vast and complex problem space within the domain of computer vision and thus requires a universally accepted baseline method with which to evaluate proposed models. While test datasets have served this purpose in the academic sphere real world application and testing of such models lacks any real comparison. Therefore we propose a framework in which models developed for FER can be compared and contrasted against one another in a constant standardized fashion. A lightweight convolutional neural network is trained on the AffectNet dataset a large variable dataset …
Adaptive Physical-Facial Representation Fusion via Subject-Invariant Cross-Modal Prompt Tuning for Video-Based Emotion Recognition
Department of Biomedical Engineering（生物医学工程系）；Anhui Province Key Laboratory of Measuring Theory and Precision Instrument（安徽省测量理论与精密仪器重点实验室）；School of Computer Science and Information Engineering（计算机科学与信息工程学院）
Imperial College London（帝国理工学院）；RealEyes（RealEyes公司）；University of Augsburg（奥格斯堡大学）
Automatic affect recognition using visual cues is an important task towards a complete interaction between humans and machines. Applications can be found in tutoring systems and human computer interaction. A critical step towards that direction is facial feature extraction. In this paper, we propose a facial feature extractor model trained on an in-the-wild and massively collected video dataset provided by the RealEyes company. The dataset consists of a million labelled frames and 2,616 thousand subjects. As temporal information is important to the emotion recognition domain, we utilise LSTM c…
Facial expression analysis has long been an active research area of computer vision. Traditional methods mainly analyse images for prototypical discrete emotions; as a result, they do not provide an accurate depiction of the complex emotional states in humans. Furthermore, illumination variance remains a challenge for face analysis in the visible light spectrum. To address these issues, we propose using a dimensional model based on valence and arousal to represent a wider range of emotions, in combination with near infra-red (NIR) imagery, which is more robust to illumination changes. Since th…
Fuzhou University（福州大学）；University of Nottingham（诺丁汉大学）；Shanghai Jiao Tong University（上海交通大学）
Facial expression is one of the most external indications of a person's feelings and emotions. In daily conversation, according to the psychologist, only 7% and 38% of information is communicated through words and sounds respective, while up to 55% is through facial expression. It plays an important role in coordinating interpersonal relationships. Ekman and Friesen recognized six essential emotions in the nineteenth century depending on a cross-cultural study, which indicated that people feel each basic emotion in the same fashion despite culture. As a branch of the field of analyzing sentime…
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）
Smile on the Face, Sadness in the Eyes: Bridging the Emotion Gap with a Multimodal Dataset of Eye and Facial Behaviors
School of Computer Science, China University of Geosciences (Wuhan)（中国地质大学（武汉）计算机科学学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）；School of Computer Science, Wuhan University（武汉大学计算机科学学院）；School of Computing, Engineering and Mathematical Sciences, La Trobe University（拉筹伯大学计算科学、工程与数学科学学院）
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…
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…
Coventry University（考文垂大学）；American University of Beirut（贝鲁特美国大学）
In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions, with up to plus or minus 60 degrees, to an illumination invariant facial representation of 0 degrees. We accomplish this by using a novel convolutional layer that exploits both local and global spatial information, and a convolutional layer with a reduced number of parameters that exploits facial symmetry. Furthermore, we introduce a generative adversarial gradual greedy layer-wise learning algorithm designed to train Adversarial Autoencoders in an efficient and incremental manner…
Université Clermont-Auvergne（克莱蒙奥弗涅大学）；CNRS（法国国家科学研究中心）；Mines de Saint-Étienne（圣艾蒂安矿业学院）；Clermont-Auvergne-INP（克莱蒙奥弗涅国立理工学院）；LIMOS（LIMOS实验室）
Understanding the facial expressions of our interlocutor is important to enrich the communication and to give it a depth that goes beyond the explicitly expressed. In fact, studying one's facial expression gives insight into their hidden emotion state. However, even as humans, and despite our empathy and familiarity with the human emotional experience, we are only able to guess what the other might be feeling. In the fields of artificial intelligence and computer vision, Facial Emotion Recognition (FER) is a topic that is still in full growth mostly with the advancement of deep learning approa…
Recently deep generative models have achieved impressive results in the field of automated facial expression editing. However, the approaches presented so far presume a discrete representation of human emotions and are therefore limited in the modelling of non-discrete emotional expressions. To overcome this limitation, we explore how continuous emotion representations can be used to control automated expression editing. We propose a deep generative model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels. One dimension rep…
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.
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 …
An automatic Facial Expression Recognition (FER) model with Adaboost face detector, feature selection based on manifold learning and synergetic prototype based classifier has been proposed. Improved feature selection method and proposed classifier can achieve favorable effectiveness to performance FER in reasonable processing time.
State University of New York at Binghamton（纽约州立大学宾汉姆顿分校）
Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes. One behavioral change is facial expression, which has been studied extensively over the past few decades. Facial behavior varies with a person's emotion according to differences in terms of culture, personality, age, context, and environment. In recent years, physiological activities have been used to study emotional responses. A typical signal is the electroencephalogram (EEG), which measures brain activity. Most of existing EEG-based em…
K. J. Somaiya College of Engineering（K. J. 索马亚工程学院）
Facial Emotion Recognition is an inherently difficult problem, due to vast differences in facial structures of individuals and ambiguity in the emotion displayed by a person. Recently, a lot of work is being done in the field of Facial Emotion Recognition, and the performance of the CNNs for this task has been inferior compared to the results achieved by CNNs in other fields like Object detection, Facial recognition etc. In this paper, we propose a multi-task learning algorithm, in which a single CNN detects gender, age and race of the subject along with their emotion. We validate this propose…
JD Digits（京东数科）；Beijing University of Posts and Telecommunications（北京邮电大学）
Much of the work on automatic facial expression recognition relies on databases containing a certain number of emotion classes and their exaggerated facial configurations (generally six prototypical facial expressions), based on Ekman's Basic Emotion Theory. However, recent studies have revealed that facial expressions in our human life can be blended with multiple basic emotions. And the emotion labels for these in-the-wild facial expressions cannot easily be annotated solely on pre-defined AU patterns. How to analyze the action units for such complex expressions is still an open question. To…
To fully understand the complexities of human emotion, the integration of multiple physical features from different modalities can be advantageous. Considering this, we present an analysis of 3D facial data, action units, and physiological data as it relates to their impact on emotion recognition. We analyze each modality independently, as well as the fusion of each for recognizing human emotion. This analysis includes which features are most important for specific emotions (e.g. happy). Our analysis indicates that both 3D facial landmarks and physiological data are encouraging for expression/…
In this paper, we describe an entry to the third Emotion Recognition in the Wild Challenge, EmotiW2015. We detail the associated experiments and show that, through more accurately locating the facial landmarks, and considering only the distances between them, we can achieve a surprising level of performance. The resulting system is not only more accurate than the challenge baseline, but also much simpler.
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…
We present Wav2Lip-Emotion, a video-to-video translation architecture that modifies facial expressions of emotion in videos of speakers. Previous work modifies emotion in images, uses a single image to produce a video with animated emotion, or puppets facial expressions in videos with landmarks from a reference video. However, many use cases such as modifying an actor's performance in post-production, coaching individuals to be more animated speakers, or touching up emotion in a teleconference require a video-to-video translation approach. We explore a method to maintain speakers' lip movement…
Samsung AI Center Cambridge（三星剑桥人工智能中心）；University of Nottingham（诺丁汉大学）
Temporal context is key to the recognition of expressions of emotion. Existing methods, that rely on recurrent or self-attention models to enforce temporal consistency, work on the feature level, ignoring the task-specific temporal dependencies, and fail to model context uncertainty. To alleviate these issues, we build upon the framework of Neural Processes to propose a method for apparent emotion recognition with three key novel components: (a) probabilistic contextual representation with a global latent variable model; (b) temporal context modelling using task-specific predictions in additio…
Training facial emotion recognition models requires large sets of data and costly annotation processes. To alleviate this problem, we developed a gamified method of acquiring annotated facial emotion data without an explicit labeling effort by humans. The game, which we named Facegame, challenges the players to imitate a displayed image of a face that portrays a particular basic emotion. Every round played by the player creates new data that consists of a set of facial features and landmarks, already annotated with the emotion label of the target facial expression. Such an approach effectively…
Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and stra…
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）
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…
Automatic recognition of emotion from facial expressions is an intense area of research, with a potentially long list of important application. Yet, the study of emotion requires knowing which facial expressions are used within and across cultures in the wild, not in controlled lab conditions; but such studies do not exist. Which and how many cross-cultural and cultural-specific facial expressions do people commonly use? And, what affect variables does each expression communicate to observers? If we are to design technology that understands the emotion of users, we need answers to these two fu…
This article describes how to deploy dihedral group theory to detect Facial Key Points (FKP) symmetry to recognize emotions. The method can be applied in many other areas which those have the same data texture.
Hochschule Bonn-Rhein-Sieg（波恩-莱茵-锡格应用技术大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
Facial emotion recognition is the task to classify human emotions in face images. It is a difficult task due to high aleatoric uncertainty and visual ambiguity. A large part of the literature aims to show progress by increasing accuracy on this task, but this ignores the inherent uncertainty and ambiguity in the task. In this paper we show that Bayesian Neural Networks, as approximated using MC-Dropout, MC-DropConnect, or an Ensemble, are able to model the aleatoric uncertainty in facial emotion recognition, and produce output probabilities that are closer to what a human expects. We also show…
Wuhan University（武汉大学）；West Virginia University（西弗吉尼亚大学）
Facial expressions of emotion play an important role in human social interactions. However, posed acting is not always the same as genuine feeling. Therefore, the credibility assessment of facial expressions, namely, the discrimination of genuine (spontaneous) expressions from posed(deliberate/volitional/deceptive) ones, is a crucial yet challenging task in facial expression understanding. Rapid progress has been made in recent years for automatic detection of genuine and posed facial expressions. This paper presents a general review of the relevant research, including several spontaneous vs. …
Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting. Among all techniques for FER, deep learning models, especially Convolutional Neural Networks (CNNs) have shown great potential due to their powerful automatic feature extraction and computational efficiency. In this work, we achieve the highest single-network classification accuracy on the FER2…
