Three Stream Graph Attention Network Using Dynamic Patch Selection for the Classification of Micro-Expressions
To understand the genuine emotions expressed by humans during social interactions, it is necessary to recognize the subtle changes on the face (micro-expressions) demonstrated by an individual. Facial micro-expressions are brief, rapid, spontaneous gestures and non-voluntary facial muscle movements beneath the skin. Therefore, it is a challenging task to classify facial micro-expressions. This paper presents an end-to-end novel three-stream graph attention network model to capture the subtle changes on the face and recognize micro-expressions (MEs) by exploiting the relationship between optical flow magnitude, optical flow direction, and the node locations features. A facial graph representational structure is used to extract the spatial and temporal information using the three frames. The varying dynamic patch size of optical flow features is used to extract the local texture information across each landmark point. The network only utilizes the landmark points location features and optical flow information across these points and generates good results for the classification of MEs. A comprehensive evaluation of SAMM and the CASME II datasets demonstrates the high efficacy, efficiency, and generalizability of the proposed approach and achieves better results than the state-of-the-art methods.