Facial micro-expression is important and prevalent as it reveals the actual emotion of humans. Especially, the automated micro-expression analysis substituted for humans begins to gain the attention recently. However, largely unsolved problems of detecting micro-expressions for subsequent analysis need to be addressed sequentially, such as subtle head movements and unconstrained lighting conditions. To face these challenges, we propose a probabilistic framework to detect spontaneous micro-expression clips temporally from a video sequence (micro-expression spotting) in this paper. In the probabilistic framework, a random walk model is presented to calculate the probability of individual frames having micro-expressions. The Adaboost model is utilized to estimate the initial probability for each frame and the correlation between frames would be considered into the random walk model. The active shape model and Procrustes analysis, which are robust to the head movement and lighting variation, are used to describe the geometric shape of human face. Then the geometric deformation would be modeled and used for Adaboost training. Through performing the experiments on two spontaneous micro-expression datasets, we verify the effectiveness of our proposed micro-expression spotting approach. 
Spontaneous micro-expression spotting via geometric deformation modeling