Journal
NEUROCOMPUTING
Volume 273, Issue -, Pages 643-649Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2017.08.043
Keywords
Facial expression recognition; Sparse autoencoders; Deep architecture; High-dimensional feature; Histogram of oriented gradients (HOG)
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Funding
- UK-China Industry Academia Partnership Programme [UK-CIAPP-276]
- Korea Foundation for Advanced Studies
- Natural Science Foundation of China [61403319]
- Fujian Natural Science Foundation [2015J05131]
- Fujian Provincial Key Laboratory of Eco-Industrial Green Technology
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Facial expression recognition is an important research issue in the pattern recognition field. In this paper, we intend to present a novel framework for facial expression recognition to automatically distinguish the expressions with high accuracy. Especially, a high-dimensional feature composed by the combination of the facial geometric and appearance features is introduced to the facial expression recognition due to its containing the accurate and comprehensive information of emotions. Furthermore, the deep sparse autoencoders (DSAE) are established to recognize the facial expressions with high accuracy by learning robust and discriminative features from the data. The experiment results indicate that the presented framework can achieve a high recognition accuracy of 95.79% on the extended Cohn-Kanade (CK+) database for seven facial expressions, which outperforms the other three state-of-the-art methods by as much as 3.17%, 4.09% and 7.41%, respectively. In particular, the presented approach is also applied to recognize eight facial expressions (including the neutral) and it provides a satisfactory recognition accuracy, which successfully demonstrates the feasibility and effectiveness of the approach in this paper. (C) 2017 Elsevier B.V. All rights reserved.
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