A main directional maximal difference analysis for spotting facial movements from long-term videos

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• 作者：Su-Jing Wang^a ; ^{wangsujing@psych.ac.cn}Author Vitae ; Shuhang Wu^bAuthor Vitae ; Xingsheng Qian^aAuthor Vitae ; Jingxiu Li^cAuthor Vitae ; Xiaolan Fu^d ; ^eAuthor Vitae
• 关键词：Micro-expression recognition ; Macro-expression ; Micro-expression spotting ; Optical flow
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：22 March 2017
• 年：2017
• 卷：230
• 期：Complete
• 页码：382-389
• 全文大小：1586 K
• 卷排序：230

文摘

There is an increasing interests in micro-expression researches. Spotting micro-expressions in long-term videos is very important, not only for providing clues for lie detection, but also for reducing the labor required to collect micro-expression data. However, little progress has been made in spotting micro-expressions. In this paper, we propose a Main Directional Maximal Difference (MDMD) Analysis for micro-expression spotting. MDMD uses the magnitude maximal difference in the main direction of optical flow features to spot facial movements, including micro-expressions. Using block structured facial regions, MDMD obtains more accurate features of movement of expressions for automatically spotting micro-expressions and macro-expressions from videos. This method involves both the temporal and spatial locations of face movements. Evaluations using the CAS(ME)2 database containing micro-expressions and macro-expressions show that MDMD is more robust than some state-of-the-art algorithms.