Time efficient micro-expression recognition using weighted spatio-temporal landmark graphs

Micro-expressions have been shown to be effective in understanding the genuine emotions of a person. While many advances have been made in detecting micro-expressions using deep learning, previous studies in recognizing micro-expressions require pre-processing steps and the use of large feature sets resulting in large runtimes and thus have limited applicability in real-world scenarios. In this paper, we propose time-efficient end-to-end framework which uses landmark-based positional features to generate spatio-temporal graphs that can be applied to micro-expression recognition using Graph Convolutional Neural Networks (GCNs). We explore the importance of landmark features and propose a selective feature reduction approach to further improve efficiency. We perform experiments using the SMIC, CASMEII and SAMM datasets and demonstrate that our approach significantly speeds up predictions and delivers results comparable to the state-of-the-art.