Facial Expression Recognition with Independent Subspace Analysis Based Feature Learning

Authors

Yongjie Zhan, 1. Center for Digital Media Computing, Software School, Xiamen University, Xiamen 361005, China;;2. School of Information Science and Engineering, Xiamen University, Xiamen 361005, China;
Long Fei, 1. Center for Digital Media Computing, Software School, Xiamen University, Xiamen 361005, China;;
Yikun Bu, 2. School of Information Science and Engineering, Xiamen University, Xiamen 361005, China;

Abstract

Abstract: Hand-designed features (such as Gabor, LBP) has been widely employed in facial expression recognition. In the real-world applications of facial expression recognition, it is very difficult to achieve perfect face alignment because of the impact of complex background and the limitations of face alignment approaches. Independent Subspace Analysis (ISA) is an unsupervised feature learning method, which can be used to learn phase-invariant visual features from images. The problem of facial expression recognition based on ISA in the situation of not precise face alignment was investigated. Through analyzing the facial expression recognition performances with different subspace size, it was turned out that choosing an appropriate subspace size is important to improve the robustness of learned features for facial expression recognition in the situation of not precise alignment.

Recommended Citation

Zhan, Yongjie; Fei, Long; and Bu, Yikun (2020) "Facial Expression Recognition with Independent Subspace Analysis Based Feature Learning," Journal of System Simulation: Vol. 27: Iss. 10, Article 14.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss10/14

First Page

2316

Revised Date

2015-07-30

DOI Link

https://doi.org/

Last Page

2319

CLC

TP391.41

Recommended Citation

Zhan Yongjie, Long Fei, Bu Yikun. Facial Expression Recognition with Independent Subspace Analysis Based Feature Learning[J]. Journal of System Simulation, 2015, 27(10): 2316-2319.

Corresponding Author

Long Fei,