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Supervised super-vector encoding for facial expression recognition
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DOI：10.1016/j.patrec.2014.05.011.png)
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Abstract
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Expression recognition from faces with varying pose and illumination conditions is a challenging research area with growing interest. In this paper, we develop a novel supervised super-vector encoding framework to learn discriminative image feature representations. The framework is then validated on the Multi-PIE and BU3D-FE databases for multi-view facial expression recognition. Extensive experiments show that our supervised framework gives significant improvement over the unsupervised counterpart and outperforms the state-of-the-arts. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
GMM learning
Face biometrics
Facial expression recognition
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