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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
3D facial expression recognition based on automatically selected features
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Hao Tang, University of Illinois at Urbana-Champaign, 61801 USA
Thomas S. Huang, University of Illinois at Urbana-Champaign, 61801 USA
In this paper, the problem of person-independent facial expression recognition from 3D facial shapes is investigated. We propose a novel automatic feature selection method based on maximizing the average relative entropy of marginalized class-conditional feature distributions and apply it to a complete pool of candidate features composed of normalized Euclidean distances between 83 facial feature points in the 3D space. Using a regularized multi-class AdaBoost classification algorithm, we achieve a 95.1% average recognition rate for six universal facial expressions on the publicly available 3D facial expression database BU-3DFE [1], with a highest average recognition rate of 99.2% for the recognition of surprise. We compare these results with the results based on a set of manually devised features and demonstrate that the auto features yield better results than the manual features. Our results outperform the results presented in the previous work [2] and [3], namely average recognition rates of 83.6% and 91.3% on the same database, respectively.
Citation:
Hao Tang, Thomas S. Huang, "3D facial expression recognition based on automatically selected features," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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