Abstract
When an emotional state is involuntarily and spontaneously delivered with low intensity and short duration, micro expression (ME) occurs. Developing from psychological perspectives to computer vision standpoints, ME has obtained huge advancements and breakthroughs. A variety of feature extraction techniques have been introduced based on different outlooks. With exclusive characteristics of ME, geometric feature learning is involved in approaching the problem in this paper. Specifically, we propose a method where dominant facial regions of interest are extracted and used to further learn spatio-temporal features with space-time auto-correlation of gradients (STACOG) technique. The facial motion features are fed into a multi-layer perceptron network for emotion classification. The combination of ROIs and STACOG captures ME with respect to geometrical property along side with the temporal aspect in three dimensional space. This puts a stress on presumably suboptimal features, making them more salient and resilient for the classification stage. The framework is experimented on three well-known, state-of-the-art spontaneous ME databases CASMEII, SMIC and SAM.