Journal
PATTERN RECOGNITION
Volume 66, Issue -, Pages 44-52Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2016.11.029
Keywords
Micro-expression recognition; Multi-task learning
Funding
- National Key Research and Development Program of China [2016YFB1001002, 2016YFB1001003]
- NSFC [61573387, 61472456, 61522115, 61661130157, 61628212]
- Guangdong Natural Science Funds for Distinguished Young Scholar [S2013050014265]
- GuangDong Program [2015B010105005]
- Guangdong Science and Technology Planning Project [2016A010102012, 2014B010118003]
- Guangdong Program for Support of Top-notch Young Professionals [2014TQ01X779]
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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.
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