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Micro-attention for micro-expression recognition
Wang, Chongyang1; Peng, Min2; Bi, Tao1; Chen, Tong3,4
2020-10-14
摘要Micro-expression, for its high objectivity in emotion detection, has emerged to be a promising modality in affective computing. Recently, deep learning methods have been successfully introduced into the micro-expression recognition area. Whilst the higher recognition accuracy achieved, substantial challenges in micro-expression recognition remain. The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior. In this work, to tackle such challenges, we propose a novel attention mechanism called micro-attention cooperating with residual network. Micro-attention enables the network to learn to focus on facial areas of interest covering different action units. Moreover, coping with small datasets, the micro-attention is designed without adding noticeable parameters while a simple yet efficient transfer learning approach is together utilized to alleviate the overfitting risk. With extensive experimental evaluations on three benchmarks (CASMEII, SAMM and SMIC) and post-hoc feature visualizations, we demonstrate the effectiveness of the proposed micro-attention and push the boundary of automatic recognition of micro-expression. (C) 2020 Elsevier B.V. All rights reserved.
关键词Micro expression recognition Deep learning Attention mechanism Transfer learning
DOI10.1016/j.neucom.2020.06.005
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号410页码:354-362
通讯作者Peng, Min(pengmin@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000579799300030
语种英语