AUTHOR=Peng Min , Wang Chongyang , Chen Tong , Liu Guangyuan , Fu Xiaolan TITLE=Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition JOURNAL=Frontiers in Psychology VOLUME=8 YEAR=2017 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2017.01745 DOI=10.3389/fpsyg.2017.01745 ISSN=1664-1078 ABSTRACT=

Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.