Abstract
This paper presents a facial expression recognition approach based on MB-LGBP feature and multi-level classification. First, the multi-scale block local Gabor binary patterns (MB-LGBP) operator is extracted to achieve both locally and globally informative features. Then a two-level classification method is proposed. At the coarse level, two expression candidates with the first two high decision confidence are selected from 7 basic expression classes based on MB-LGBP features. At the fine level, one of the two candidate classes is verified as final expression class based on more delicate 2D MB-LGBP features. The promising result proves the superiority of our method to some other popular paradigms in expression recognition.
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Zhang, Z., Xu, C., Wang, J., Chen, X. (2011). Facial Expression Recognition Based on MB-LGBP Feature and Multi-level Classification. In: Jin, D., Lin, S. (eds) Advances in Multimedia, Software Engineering and Computing Vol.2. Advances in Intelligent and Soft Computing, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25986-9_6
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DOI: https://doi.org/10.1007/978-3-642-25986-9_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25985-2
Online ISBN: 978-3-642-25986-9
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