1. Recognizing Emotions From an Ensemble of Features.
- Author
-
Tariq, Usman, Lin, Kai-Hsiang, Li, Zhen, Zhou, Xi, Wang, Zhaowen, Le, Vuong, Huang, Thomas S., Lv, Xutao, and Han, Tony X.
- Subjects
- *
FEATURE extraction , *HUMAN facial recognition software , *COMPUTER vision , *PERFORMANCE evaluation , *SYSTEM identification , *SUPPORT vector machines - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF