Multi-task mid-level feature learning for micro-expression recognition

Due to the short duration and low intensity of micro-expressions, the recognition of micro-expression is still a challenging problem. In this paper, we develop a novel multi-task mid-level feature learning method to enhance the discrimination ability of extracted low-level features by learning a set of class-specific feature mappings, which would be used for generating our mid-level feature representation. Moreover, two weighting schemes are employed to concatenate different mid-level features. We also construct a new mobile micro-expression set to evaluate the performance of the proposed mid-level feature learning framework. The experimental results on two widely used non-mobile micro-expression datasets and one mobile micro-expression set demonstrate that the proposed method can generally improve the performance of the low-level features, and achieve comparable results with the state-of-the-art methods. HighlightsA multi-task mid-level feature learning framework was proposed to improve the discrimination ability of low-level features.A new micro-expression database captured by mobile devices was collected.Extensive experiments are conducted to illustrate that our method can improve the performance of existing low-level features.