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Sparse MDMO: learning a discriminative feature for micro-expression recognition

Liu, Yong-Jin, Li, Bing-Jun and Lai, Yukun ORCID: 2021. Sparse MDMO: learning a discriminative feature for micro-expression recognition. IEEE Transactions on Affective Computing 12 (1) , pp. 254-261. 10.1109/TAFFC.2018.2854166

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Micro-expressions are the rapid movements of facial muscles that can be used to reveal concealed emotions. Recognizing them from video clips has a wide range of applications and receives increasing attention recently. Among existing methods, the main directional mean optical-flow (MDMO) feature achieves state-of-the-art performance for recognizing spontaneous micro-expressions. For a video clip, the MDMO feature is computed by averaging a set of atomic features frame-by-frame. Despite its simplicity, the average operation in MDMO can easily lose the underlying manifold structure inherent in the feature space. In this paper we propose a sparse MDMO feature that learns an effective dictionary from a micro-expression video dataset. In particular, a new distance metric is proposed based on the sparsity of sample points in the MDMO feature space, which can efficiently reveal the underlying manifold structure. The proposed sparse MDMO feature is obtained by incorporating this new metric into the classic graph regularized sparse coding (GraphSC) scheme. We evaluate sparse MDMO and four representative features (LBP-TOP, STCLQP, MDMO and FDM) on three spontaneous micro-expression datasets (SMIC, CASME and CASME II). The results show that sparse MDMO outperforms these representative features.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1949-3045
Funders: Royal Society
Date of First Compliant Deposit: 29 June 2018
Date of Acceptance: 24 June 2018
Last Modified: 06 Nov 2023 20:57

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