Spatiotemporal features selection for spontaneous micro-expression recognition

Abstract

Micro-expressions reveal a person’s true feelings and motivations, and they can be so subtle that they are difficult to recognize. Micro-expression recognition can be used for criminal justice and national security. The existing methods usually extract the same micro-expression features from three different orthogonal planes, but cannot accurately reflect the characteristics of different space features, so the actual recognition rate is low. According to the motion and appearance information of micro-expression sequences in the temporal domain and spatial domain, this paper proposes a micro-expression recognition method based on spatiotemporal features selection. The Improved Local Directional Number Pattern (ILDNP) in the spatial domain and the Pyramid of Histograms of Orientation Gradients without Edge Extraction (PHOG-WEE) in the temporal domain are extracted individually and fused in the spatiotemporal domain to be a micro-expression feature, and then classified by the Support Vector Machine (SVM). Compared with the state-of-the-art LBP-TOP, HOG-TOP, HIGO-TOP, LBPSIP and Gabor algorithms on the SMIC, CASME and CASME II databases, experiments show that the proposed method has a better performance in describing the texture features of different planes, as well as a higher recognition rate on micro-expressions.