A Delaunay-Based Temporal Coding Model for Micro-expression Recognition

Micro-expression recognition has been a challenging problem in computer vision research due to its briefness and subtlety. Previous psychological study shows that even human being can only recognize micro-expressions with low average recognition rates. In this paper, we propose an effective and efficient method to encode the micro-expressions for recognition. The proposed method, referred to as Delaunay-based temporal coding model (DTCM), encodes texture variations corresponding to muscle activities on face due to dynamical micro-expressions. Image sequences of micro-expressions are normalized not only temporally but also spatially based on Delaunay triangulation, so that the influence of personal appearance irrelevant to micro-expressions can be suppressed. Encoding temporal variations at local subregions and selecting spatial salient subregions in the face area escalates the capacity of our method to locate spatiotemporally important features related to the micro-expressions of interest. Extensive experiments on publicly available datasets, including SMIC, CASME, and CASME II, verified the effectiveness of the proposed model.

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