Less is more: Micro-expression recognition from video using apex frame


Liong, Sze Teng and See, John Su Yang and Wong, Kok Sheik and Phan, Raphael Chung Wei (2018) Less is more: Micro-expression recognition from video using apex frame. Signal Processing: Image Communication, 62. pp. 82-92. ISSN 0923-5965

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Despite recent interest and advances in facial micro-expression research, there is still plenty of room for improvement in terms of micro-expression recognition. Conventional feature extraction approaches for micro-expression video consider either the whole video sequence or a part of it, for representation. However, with the high-speed video capture of micro-expressions (100–200 fps), are all frames necessary to provide a sufficiently meaningful representation? Is the luxury of data a bane to accurate recognition? A novel proposition is presented in this paper, whereby we utilize only two images per video, namely, the apex frame and the onset frame. The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression. A new feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to encode essential expressiveness of the apex frame. We evaluated the proposed method on five micro-expression databases—CAS(ME), CASME II, SMIC-HS, SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with our proposed technique achieving a state-of-the-art F1-score recognition performance of 0.61 and 0.62 in the high frame rate CASME II and SMIC-HS databases respectively.

Item Type: Article
Uncontrolled Keywords: Human face recognition (Computer science), Emotion, Apex, Optical flow, Optical strain, Recognition
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 11 Nov 2020 14:45
Last Modified: 11 Nov 2020 14:45
URII: http://shdl.mmu.edu.my/id/eprint/7350


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