Research on facial expression recognition (FER) technology can promote the development of theoretical and practical applications for our daily life. Currently, most of the related works on this technology are focused on un-occluded FER. However, in real life, facial expression images often have partial occlusion; therefore, the accurate recognition of occluded facial expression images is a topic that should be explored. In this paper, we proposed a novel Wasserstein generative adversarial network-based method to perform occluded FER. After complementing the face occlusion image with complex facial expression information, the recognition is achieved by learning the facial expression features of the images. This method consists of a generator G and two discriminators D
 1
 and D
 2
. The generator naturally complements occlusion in the expression image under the triple constraints of weighted reconstruction loss l
 wr
, triplet loss l
 t
, and adversarial loss l
 a
. We optimize the discriminator D
 1
 to distinguish between real and fake by constructing an adversarial loss l
 a
 between the generated complementing images, original un-occluded images, and small-scale-occluded images based on the Wasserstein distance. Finally, the FER is completed by introducing classification loss l
 c
 into D
 2
. To verify the effectiveness of the proposed method, an experimental analysis was performed on the AffectNet and RAF-DB datasets. The visual occlusion complementing results, comparison of recognition rates of facial expression images with and without de-occlusion processing, and T-distributed stochastic neighbor embedding visual analysis of facial expression features all prove the effectiveness of the proposed method. The experimental results show that the proposed method is better than the existing state-of-the-art methods.

/pdf/wgan-based-robust-occluded-facial-expression-recognition-3lcv1ka8ch.pdf

WGAN-Based Robust Occluded Facial Expression Recognition

As one of the most important affective signals, facial affect analysis (FAA) is essential for developing human-computer interaction systems. Early methods focus on extracting appearance and geometry features associated with human affects while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. This paper provides a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their non-deep or deep learning methods, emphasizing the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field. 

/pdf/graph-based-facial-affect-analysis-a-review-12rcjsvf.pdf

Graph-based Facial Affect Analysis: A Review

Facial action unit (AU) recognition has attracted increasing attention due to its indispensable role in affective computing, especially in the field of affective human-computer interaction. Due to the subtle and transient nature of AU, it is challenging to capture the delicate and ambiguous motions in local facial regions among consecutive frames. Considering that context is essential to resolve ambiguity in human visual system, modeling context within or among facial images emerges as a promising approach for AU recognition task. To this end, we propose CaFGraph, a novel context-aware facial multi-graph that can model both morphological & muscular-based region-level local context and region-level temporal context. CaFGraph is the first work to construct a universal facial multi-graph structure that is independent of both task settings and dataset statistics for almost all fine-grained facial behavior analysis tasks, including but not limited to AU recognition. To make full use of the context, we then present CaFNet that learns context-aware facial graph representations via CaFGraph from facial images for multi-label AU recognition. Experiments on two widely used benchmark datasets, BP4D and DISFA, demonstrate the superiority of our CaFNet over the state-of-the-art methods.

CaFGraph: Context-aware Facial Multi-graph Representation for Facial Action Unit Recognition

In this letter, we focus on designing an effective method for lightweight and accurate facial action unit (AU) detection, which is essential for emotional communication in most human-robot interaction scenarios. AU detection is a delicate and challenging task because the subtle fleeting appearance changes caused by AUs are very difficult to catch and express. Therefore existing approaches mainly deal with static facial states or frame-level temporal relationships. The dynamic process of facial muscle movement, as the core feature of AU, is yet ignored and rarely exploited by prior studies. Based on such observation, we propose Flow Supervised Module (FSM) to explicitly capture the dynamic facial movement in the form of Flow and use the learned Flow to provide supervision signals for the detection model during the training stage effectively and efficiently. Furthermore, the proposed FSM can be easily incorporated into various backbone networks and boost their performance. Extensive experiments are conducted on two benchmark datasets, DISFA and BP4D, showing state-of-the-art performance with competitive detection speed.

Cross-Modal Representation Learning for Lightweight and Accurate Facial Action Unit Detection

Multimodal Sentiment Analysis (MSA) aims to understand human intentions by integrating emotion-related clues from diverse modalities, such as visual, language, and audio. Unfortunately, the current MSA task invariably suffers from unplanned dataset biases, particularly multimodal utterance-level label bias and word-level context bias. These harmful biases potentially mislead models to focus on statistical shortcuts and spurious correlations, causing severe performance bottlenecks. To alleviate these issues, we present a Multimodal Counterfactual Inference Sentiment (MCIS) analysis framework based on causality rather than conventional likelihood. Concretely, we first formulate a causal graph to discover harmful biases from already-trained vanilla models. In the inference phase, given a factual multimodal input, MCIS imagines two counterfactual scenarios to purify and mitigate these biases. Then, MCIS can make unbiased decisions from biased observations by comparing factual and counterfactual outcomes. We conduct extensive experiments on several standard MSA benchmarks. Qualitative and quantitative results show the effectiveness of the proposed framework.

Towards Multimodal Sentiment Analysis Debiasing via Bias Purification

Emotional recognition based on facial expressions is a very active research topic in the field of human-computer interaction. There has been a great body of work with in-depth of study in this area. In this paper, we analyze and compare the state-of-the-art facial expression recognition methods, propose some evaluation dimensions and discuss possible directions for future research.

A Comparison of Methods of Facial Expression Recognition

With the development of affective computing, emotion recognition based on facial expression recognition has become an attractive topic in human-robot interaction. We hope that affective intelligent robots can detect and identify human facial expressions through vision, and make different responses accordingly. In this paper, we compare different facial expression recognition methods that use traditional machine learning models or deep learning models, and propose a fast and accurate multi-model facial expression recognition method for affective intelligence robots to complete the real-time and high-precision facial expression recognition task.

A Fast and Accurate Multi-Model Facial Expression Recognition Method for Affective Intelligent Robots

With the increasing need for emotion analysis, facial action unit (AU) recognition has attracted much more attention as a fundamental task for affective computing. Although deep learning has boosted the performance of AU recognition to a new level in recent years, it remains challenging to extract subject-consistent representations since the appearance changes caused by AUs are subtle and ambiguous among subjects. We observe that there are three kinds of inherent relations among AUs, which can be treated as strong prior knowledge, and pursuing the consistency of such knowledge is the key to learning subject-consistent representations. To this end, we propose a supervised hierarchical contrastive learning method (SupHCL) for AU recognition to pursue knowledge consistency among different facial images and different AUs, which is orthogonal to methods focusing on network architecture design. Specifically, SupHCL contains three relation consistency modules, i.e., unary, binary, and multivariate relation consistency modules, which take the corresponding kind of inherent relations as extra supervision to encourage knowledge-consistent distributions of both AU-level and image-level representations. Experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, demonstrate the effectiveness of each relation consistency module and the superiority of SupHCL.

Pursuing Knowledge Consistency: Supervised Hierarchical Contrastive Learning for Facial Action Unit Recognition

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