Multi-view facial expression recognition analysis with generic sparse coding feature

Authors:
Usman Tariq;Jianchao Yang;Thomas S. Huang
Affiliations:
Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA,Beckman Institute for Advanced Science and Technology ...;Department of Electrical and Computer Eng., Coordinated Science Laboratory, Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA,Beckman Institute for Advanced Science and Techn., Univ. of Illin ...;Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA,Beckman Institute for Advanced Science and Technology ...
Venue:
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Year:
2012

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Abstract

Expression recognition from non-frontal faces is a challenging research area with growing interest. This paper works with a generic sparse coding feature, inspired from object recognition, for multi-view facial expression recognition. Our extensive experiments on face images with seven pan angles and five tilt angles, rendered from the BU-3DFE database, achieve state-of-the-art results. We achieve a recognition rate of 69.1% on all images with four expression intensity levels, and a recognition performance of 76.1% on images with the strongest expression intensity. We then also present detailed analysis of the variations in expression recognition performance for various pose changes.