Long Video Micro-expression Spotting Based On OCC Theory

The generation of micro-expression (ME) is unconscious and unaffected by human subjective consciousness. It can reveal hidden emotions in humans, which has important applications in the domains of psychological diagnosis, lie detection, and so on. ME detection, which aims to locate ME apex frames in long videos, is the foundation of ME recognition. In this paper, we propose a ME detection method based on One-Class Classification (OCC) theory for long videos. The method is the first to use neutral expression frames to detect MEs, which addresses the problem of insufficient training samples in ME detection. We design a geometric motion feature based on the prior knowledge from the FACS and utilize it in conjunction with the features extracted by CNN to train separate one-class classifiers for ME detection. Finally, we evaluate the proposed method on publicly available datasets CASME I, CASME II, SAMM, and a mixed database comprising data from the three datasets, to verify the feasibility of the proposed method in both single-database and cross-database ME detection. The experimental results show that our method outperforms three other ME detection methods, demonstrating its ability to more accurately locate ME apex frames in long videos.