Multi-scale fusion visual attention network for facial micro-expression recognition

IntroductionMicro-expressions are facial muscle movements that hide genuine emotions. In response to the challenge of micro-expression low-intensity, recent studies have attempted to locate localized areas of facial muscle movement. However, this ignores the feature redundancy caused by the inaccurate locating of the regions of interest.MethodsThis paper proposes a novel multi-scale fusion visual attention network (MFVAN), which learns multi-scale local attention weights to mask regions of redundancy features. Specifically, this model extracts the multi-scale features of the apex frame in the micro-expression video clips by convolutional neural networks. The attention mechanism focuses on the weights of local region features in the multi-scale feature maps. Then, we mask operate redundancy regions in multi-scale features and fuse local features with high attention weights for micro-expression recognition. The self-supervision and transfer learning reduce the influence of individual identity attributes and increase the robustness of multi-scale feature maps. Finally, the multi-scale classification loss, mask loss, and removing individual identity attributes loss joint to optimize the model.ResultsThe proposed MFVAN method is evaluated on SMIC, CASME II, SAMM, and 3DB-Combined datasets that achieve state-of-the-art performance. The experimental results show that focusing on local at the multi-scale contributes to micro-expression recognition.DiscussionThis paper proposed MFVAN model is the first to combine image generation with visual attention mechanisms to solve the combination challenge problem of individual identity attribute interference and low-intensity facial muscle movements. Meanwhile, the MFVAN model reveal the impact of individual attributes on the localization of local ROIs. The experimental results show that a multi-scale fusion visual attention network contributes to micro-expression recognition

Automatic inference of latent emotion from spontaneous facial micro-expressions

Emotional states exert a profound influence on individuals' overall well-being, impacting them both physically and psychologically. Accurate recognition and comprehension of human emotions represent a crucial area of scientific exploration. Facial expressions, vocal cues, body language, and physiological responses provide valuable insights into an individual's emotional state, with facial expressions being universally recognised as dependable indicators of emotions. This thesis centres around three vital research aspects concerning the automated inference of latent emotions from spontaneous facial micro-expressions, seeking to enhance and refine our understanding of this complex domain. Firstly, the research aims to detect and analyse activated Action Units (AUs) during the occurrence of micro-expressions. AUs correspond to facial muscle movements. Although previous studies have established links between AUs and conventional facial expressions, no such connections have been explored for micro-expressions. Therefore, this thesis develops computer vision techniques to automatically detect activated AUs in micro-expressions, bridging a gap in existing studies. Secondly, the study explores the evolution of micro-expression recognition techniques, ranging from early handcrafted feature-based approaches to modern deep-learning methods. These approaches have significantly contributed to the field of automatic emotion recognition. However, existing methods primarily focus on capturing local spatial relationships, neglecting global relationships between different facial regions. To address this limitation, a novel third-generation architecture is proposed. This architecture can concurrently capture both short and long-range spatiotemporal relationships in micro-expression data, aiming to enhance the accuracy of automatic emotion recognition and improve our understanding of micro-expressions. Lastly, the thesis investigates the integration of multimodal signals to enhance emotion recognition accuracy. Depth information complements conventional RGB data by providing enhanced spatial features for analysis, while the integration of physiological signals with facial micro-expressions improves emotion discrimination. By incorporating multimodal data, the objective is to enhance machines' understanding of latent emotions and improve latent emotion recognition accuracy in spontaneous micro-expression analysis

Deep human face analysis and modelling

Human face appearance and motion play a significant role in creating the complex social environments of human civilisation. Humans possess the capacity to perform facial analysis and come to conclusion such as the identity of individuals, understanding emotional state and diagnosing diseases. The capacity though is not universal for the entire population, where there are medical conditions such prosopagnosia and autism which can directly affect facial analysis capabilities of individuals, while other facial analysis tasks require specific traits and training to perform well. This has lead to the research of facial analysis systems within the computer vision and machine learning fields over the previous decades, where the aim is to automate many facial analysis tasks to a level similar or surpassing humans. While breakthroughs have been made in certain tasks with the emergence of deep learning methods in the recent years, new state-of-the-art results have been achieved in many computer vision and machine learning tasks. Within this thesis an investigation into the use of deep learning based methods for facial analysis systems takes place, following a review of the literature specific facial analysis tasks, methods and challenges are found which form the basis for the research findings presented. The research presented within this thesis focuses on the tasks of face detection and facial symmetry analysis specifically for the medical condition facial palsy. Firstly an initial approach to face detection and symmetry analysis is proposed using a unified multi-task Faster R-CNN framework, this method presents good accuracy on the test data sets for both tasks but also demonstrates limitations from which the remaining chapters take their inspiration. Next the Integrated Deep Model is proposed for the tasks of face detection and landmark localisation, with specific focus on false positive face detection reduction which is crucial for accurate facial feature extraction in the medical applications studied within this thesis. Evaluation of the method on the Face Detection Dataset and Benchmark and Annotated Faces in-the-Wild benchmark data sets shows a significant increase of over 50% in precision against other state-of-the-art face detection methods, while retaining a high level of recall. The task of facial symmetry and facial palsy grading are the focus of the finals chapters where both geometry-based symmetry features and 3D CNNs are applied. It is found through evaluation that both methods have validity in the grading of facial palsy. The 3D CNNs are the most accurate with an F1 score of 0.88. 3D CNNs are also capable of recognising mouth motion for both those with and without facial palsy with an F1 score of 0.82