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Journal of Electronic Imaging

Robust facial expression recognition algorithm based on local metric learning
Author(s): Bin Jiang; Kebin Jia
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Paper 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.

Paper Details

Date Published: 3 February 2016
PDF: 8 pages
J. Electron. Imaging. 25(1) 013022 doi: 10.1117/1.JEI.25.1.013022
Published in: Journal of Electronic Imaging Volume 25, Issue 1
Show Author Affiliations
Bin Jiang, Zhengzhou Univ. of Light Industry (China)
Kebin Jia, Beijing Univ. of Technology (China)


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