title: Recognizing Emotions From an Ensemble of Features.
abstract: 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.
authors.forename: U
authors.initials: U
authors.lastname: Tariq
authors.lastname: Kai-Hsiang Lin
authors.lastname: Zhen Li
authors.lastname: Xi Zhou
authors.lastname: Zhaowen Wang
authors.lastname: Vuong Le
authors.forename: T S
authors.initials: TS
authors.lastname: Huang
authors.lastname: Xutao Lv
authors.forename: T X
authors.initials: TX
authors.lastname: Han