The performance study of facial expression recognition via sparse representation

• Published in: 2010 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 824 - 827
• Main Authors: Zhe-Wei Wang, Ming-Wei Huang, Zi-Lu Ying
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2010
• Subjects: Classification algorithms; Curvelet transform; Face recognition; Facial expression recognition; Noise; Principal component analysis; Robustness; Signal processing algorithms; Sparse representation; SRC; Training; Two-dimensional PCA
• ISBN: 9781424465262; 1424465265
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2010.5580585

Sparse representation in compressed sensing is a hot topic in signal processing and artificial intelligence due to its success in various applications. A general classification algorithm based on sparse representation theory named Sparse Representation Classification (SRC) was successfully applied in face recognition. In this paper, we research the issue of facial expression recognition (FER) via SRC. Extensive experiments of FER via SRC algorithm are carried out to study the performance of sparse representation theory for FER. The comparison of SRC algorithm with various traditional algorithms such as two-dimensional PCA as well as curvelet transform for FER is also given. Support vector machine is used for expression classification. The paper also studies the robustness of SRC algorithm for FER to noises. Satisfactory results are obtained for FER via sparse representation. The experiment results show the effectiveness of SRC algorithm on FER.