Multi-view Facial Expression Recognition Analysis with Generic Sparse Coding Feature

Usman Tariq^1,2, Jianchao Yang^1,2,3, and Thomas S. Huang^1,2

¹Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
utariq2@ifp.illinois.edu
jyang29@ifp.illinois.edu
huang@ifp.illinois.edu
http://www.illinois.edu

²Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

³Adobe Systems Incorporated, San Jose, CA 95110, USA
http://www.adobe.com

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.

LNCS 7585, p. 578 ff.

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