Micro-attention for micro-expression recognition

Micro-expression, for its high objectivity in emotion detection, has emerged to be a promising modality in affective computing. Recently, deep learning methods have been successfully introduced into the micro-expression recognition area. Whilst the higher recognition accuracy achieved, substantial challenges in micro-expression recognition remain. The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior. In this work, to tackle such challenges, we propose a novel attention mechanism called micro-attention cooperating with residual network. Micro-attention enables the network to learn to focus on facial areas of interest covering different action units. Moreover, coping with small datasets, the micro-attention is designed without adding noticeable parameters while a simple yet efficient transfer learning approach is together utilized to alleviate the overfitting risk. With extensive experimental evaluations on three benchmarks (CASMEII, SAMM and SMIC) and post-hoc feature visualizations, we demonstrate the effectiveness of the proposed micro-attention and push the boundary of automatic recognition of micro-expression

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

Facial Micro- and Macro-Expression Spotting and Generation Methods

Facial micro-expression (ME) recognition requires face movement interval as input, but computer methods in spotting ME are still underperformed. This is due to lacking large-scale long video dataset and ME generation methods are in their infancy. This thesis presents methods to address data deficiency issues and introduces a new method for spotting macro- and micro-expressions simultaneously. This thesis introduces SAMM Long Videos (SAMM-LV), which contains 147 annotated long videos, and develops a baseline method to facilitate ME Grand Challenge 2020. Further, a reference-guided style transfer of StarGANv2 is experimented on SAMM-LV to generate a synthetic dataset, namely SAMM-SYNTH. The quality of SAMM-SYNTH is evaluated by using facial action units detected by OpenFace. Quantitative measurement shows high correlations on two Action Units (AU12 and AU6) of the original and synthetic data. In facial expression spotting, a two-stream 3D-Convolutional Neural Network with temporal oriented frame skips that can spot micro- and macro-expression simultaneously is proposed. This method achieves state-of-the-art performance in SAMM-LV and is competitive in CAS(ME)2, it was used as the baseline result of ME Grand Challenge 2021. The F1-score improves to 0.1036 when trained with composite data consisting of SAMM-LV and SAMMSYNTH. On the unseen ME Grand Challenge 2022 evaluation dataset, it achieves F1-score of 0.1531. Finally, a new sequence generation method to explore the capability of deep learning network is proposed. It generates spontaneous facial expressions by using only two input sequences without any labels. SSIM and NIQE were used for image quality analysis and the generated data achieved 0.87 and 23.14. By visualising the movements using optical flow value and absolute frame differences, this method demonstrates its potential in generating subtle ME. For realism evaluation, the generated videos were rated by using two facial expression recognition networks