Multi-task mid-level feature learning for micro-expression recognition. (June 2017)

• Record Type: Journal Article
• Authors: He, Jiachi; Hu, Jian-Fang; Lu, Xi; Zheng, Wei-Shi
• Is Part Of: Pattern recognition. Volume 66(2017:Jun.)
• Page Start: 44
• Page End: 52
• Publication Date: 2017-06
• Subjects: Micro-expression recognition -- Multi-task learning; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.11.029
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: 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. Abstract : Highlights: A 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.