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Expression recognition from 3D dynamic faces using robust spatio-temporal shape features

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3 Author(s)
Vuong Le ; Dept. of Electr. & Comput. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA ; Hao Tang ; Huang, T.S.

This paper proposes a new method for comparing 3D facial shapes using facial level curves. The pair- and segment-wise distances between the level curves comprise the spatio-temporal features for expression recognition from 3D dynamic faces. The paper further introduces universal background modeling and maximum a posteriori adaptation for hidden Markov models, leading to a decision boundary focus classification algorithm. Both techniques, when combined, yield a high overall recognition accuracy of 92.22% on the BU-4DFE database in our preliminary experiments. Noticeably, our feature extraction method is very efficient, requiring simple preprocessing, and robust to variations of the input data quality.

Published in:

Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on

Date of Conference:

21-25 March 2011