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Recognizing Emotions From an Ensemble of Features
Tariq, Usman1,4; Lin, Kai-Hsiang1,4; Li, Zhen1,4; Zhou, Xi2; Wang, Zhaowen1,4; Le, Vuong1,4; Huang, Thomas S.1,4; Lv, Xutao3; Han, Tony X.3
2012-08-01
摘要This paper details the authors' efforts to push the baseline of emotion recognition performance on the Geneva Multimodal Emotion Portrayals (GEMEP) Facial Expression Recognition and Analysis database. Both subject-dependent and subject-independent emotion recognition scenarios are addressed in this paper. The approach toward solving this problem involves face detection, followed by key-point identification, then feature generation, and then, finally, classification. An ensemble of features consisting of hierarchical Gaussianization, scale-invariant feature transform, and some coarse motion features have been used. In the classification stage, we used support vector machines. The classification task has been divided into person-specific and person-independent emotion recognitions using face recognition with either manual labels or automatic algorithms. We achieve 100% performance for the person-specific one, 66% performance for the person-independent one, and 80% performance for overall results, in terms of classification rate, for emotion recognition with manual identification of subjects.
关键词Biometrics computer vision emotion recognition machine vision
DOI10.1109/TSMCB.2012.2194701
发表期刊IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
ISSN1083-4419
卷号42期号:4页码:1017-1026
通讯作者Tariq, U (reprint author), Univ Illinois, Dept Elect & Comp Engn, Coordinated Sci Lab, Urbana, IL 61801 USA.
收录类别SCI
WOS记录号WOS:000308995000005
语种英语