A fast and robust feature set for cross individual facial expression recognition

Authors:
Rodrigo Araujo;Yun-Qian Miao;Mohamed S. Kamel;Mohamed Cheriet
Affiliations:
Center for Pattern Analysis and Machine Intelligence, Electrical & Computer Engineering, University of Waterloo, Canada;Center for Pattern Analysis and Machine Intelligence, Electrical & Computer Engineering, University of Waterloo, Canada;Center for Pattern Analysis and Machine Intelligence, Electrical & Computer Engineering, University of Waterloo, Canada;Department of Automated Manufacturing Engineering, École de Technologie Supérieure, Canada
Venue:
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Year:
2012

Citing 9
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Abstract

This paper presents a new simple and robust set of features to classify emotional states in sequences of facial images. The proposed method is derived from simple geometric-based features that deliver a fast, highly discriminative, low-dimensional, and robust classification across individuals. The proposed method was compared to other state-of-the-art methods such as Gabor, LBP and AAM-based features. They were all compared using four different classifiers and experimental results based on these classifiers have shown that the proposed features are more stable in "leave-same-sequence-image-out" (LSSIO) environments, less computational intense and faster when compared to others.