Efficient spatio-temporal local binary patterns for spontaneous facial micro-expression recognition

Yandan Wang, John See, Raphael C.W. Phan, Yee Hui Oh

Research output: Contribution to journalArticleResearchpeer-review

71 Citations (Scopus)

Abstract

Micro-expression recognition is still in the preliminary stage, owing much to the numerous difficulties faced in the development of datasets. Since micro-expression is an important affective clue for clinical diagnosis and deceit analysis, much effort has gone into the creation of these datasets for research purposes. There are currently two publicly available spontaneous micro-expression datasets - SMIC and CASME II, both with baseline results released using the widely used dynamic texture LBP-TOP for feature extraction. Although LBP-TOP is popular and widely used, it is still not compact enough. In this paper, we draw further inspiration from the concept of LBP-TOP that considers three orthogonal planes by proposing two efficient approaches for feature extraction. The compact robust form described by the proposed LBP-Six Intersection Points (SIP) and a super-compact LBP-Three Mean Orthogonal Planes (MOP) not only preserves the essential patterns, but also reduces the redundancy that affects the discriminality of the encoded features. Through a comprehensive set of experiments, we demonstrate the strengths of our approaches in terms of recognition accuracy and efficiency.

Original languageEnglish
Article numbere0124674
JournalPLoS ONE
Volume10
Issue number5
DOIs
Publication statusPublished - 19 May 2015
Externally publishedYes

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