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Robust facial expression recognition algorithm based on local metric learning

[+] Author Affiliations
Bin Jiang

Zhengzhou University of Light Industry, College of Computer and Communication Engineering, Zhengzhou 450002, China

Kebin Jia

Beijing University of Technology, College of Electronic Information and Control Engineering, Beijing 100124, China

J. Electron. Imaging. 25(1), 013022 (Feb 03, 2016). doi:10.1117/1.JEI.25.1.013022
History: Received September 24, 2015; Accepted January 8, 2016
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Abstract.  In facial expression recognition tasks, different facial expressions are often confused with each other. Motivated by the fact that a learned metric can significantly improve the accuracy of classification, a facial expression recognition algorithm based on local metric learning is proposed. First, k-nearest neighbors of the given testing sample are determined from the total training data. Second, chunklets are selected from the k-nearest neighbors. Finally, the optimal transformation matrix is computed by maximizing the total variance between different chunklets and minimizing the total variance of instances in the same chunklet. The proposed algorithm can find the suitable distance metric for every testing sample and improve the performance on facial expression recognition. Furthermore, the proposed algorithm can be used for vector-based and matrix-based facial expression recognition. Experimental results demonstrate that the proposed algorithm could achieve higher recognition rates and be more robust than baseline algorithms on the JAFFE, CK, and RaFD databases.

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Citation

Bin Jiang and Kebin Jia
"Robust facial expression recognition algorithm based on local metric learning", J. Electron. Imaging. 25(1), 013022 (Feb 03, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.1.013022


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