Journal
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
Volume 7, Issue 4, Pages 299-310Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2015.2485205
Keywords
Micro-expression; optical flow; recognition; feature
Funding
- National Natural Science Foundation of China [61322206, 61521002, 61379095, 61375009]
- Beijing Natural Science Foundation [4152055]
- Open Projects Program of National Laboratory of Pattern Recognition [201306295]
- TNList Cross-discipline Foundation
- Academy of Finland
- Infotech Oulu
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Micro-expressions are brief facial movements characterized by short duration, involuntariness and low intensity. Recognition of spontaneous facial micro-expressions is a great challenge. In this paper, we propose a simple yet effective Main Directional Mean Optical-flow (MDMO) feature for micro-expression recognition. We apply a robust optical flow method on micro-expression video clips and partition the facial area into regions of interest (ROIs) based partially on action units. The MDMO is a ROI-based, normalized statistic feature that considers both local statistic motion information and its spatial location. One of the significant characteristics of MDMO is that its feature dimension is small. The length of a MDMO feature vector is 36 x 2 = 72, where 36 is the number of ROIs. Furthermore, to reduce the influence of noise due to head movements, we propose an optical-flow-driven method to align all frames of a micro-expression video clip. Finally, a SVM classifier with the proposed MDMO feature is adopted for micro-expression recognition. Experimental results on three spontaneous micro-expression databases, namely SMIC, CASME and CASME II, show that the MDMO can achieve better performance than two state-of-the-art baseline features, i.e., LBP-TOP and HOOF.
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