Effective micro-expression recognition using relaxed K-SVD algorithm View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-12

AUTHORS

Hao Zheng, Jie Zhu, Zhongxue Yang, Zhong Jin

ABSTRACT

Regarded as its subtlety, micro-expression is a challenging research problem. In the paper, we propose a relax K-SVD classifier (RK-SVD) for micro-expression recognition. RK-SVD minimizes the variance of sparse coefficients to address the similarity of same classes and the distinctiveness of different classes in sparse coefficients. In addition, reconstruction error and classification error are also considered. The optimization is implemented by the K-SVD algorithm and stochastic gradient descent algorithm. Finally a single overcomplete dictionary and an optimal linear classifier are learned simultaneously. We show that RK-SVD can effectively recognize micro-expression under three spontaneous micro-expression datasets including SMIC, CASME, and CASME II. More... »

PAGES

2043-2049

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13042-017-0684-6

DOI

http://dx.doi.org/10.1007/s13042-017-0684-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085052221


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