CFD: A Collaborative Feature Difference Method for Spontaneous Micro-Expression Spotting

Micro-expression (ME) is a special type of human expression which can reveal the real emotion that people want to conceal. Spontaneous ME (SME) spotting is to identify the subsequences containing SMEs from a long facial video. The study of SME spotting has a significant importance, but is also very challenging due to the fact that in real-world scenarios, SMEs may occur along with normal facial expressions and other prominent motions such as head movements. In this paper, we improve a state-of-the-art SME spotting method called feature difference analysis (FD) in the following two aspects. First, FD relies on a partitioning of facial area into uniform regions of interest (ROIs) and computing features of a selected sequence. We propose a novel evaluation method by utilizing the Fisher linear discriminant to assign a weight for each ROI, leading to more semantically meaningful ROIs. Second, FD only considers two features (LBP and HOOF) independently. We introduce a state-of-the-art MDMO feature into FD and propose a simple yet efficient collaborative strategy to work with two complementary features, i.e., LBP characterizing texture information and MDMO characterizing motion information. We call our improved FD method collaborative feature difference (CFD). Experimental results on two well-established SME datasets SMIC-E and CASME II show that CFD significantly improves the performance of the original FD.