Infrared face recognition using linear subspace analysis

Wei Ge; Dawei Wang; Yuqi Cheng; Ming Zhu

doi:10.1117/12.832984

30 October 2009 Infrared face recognition using linear subspace analysis

Wei Ge, Dawei Wang, Yuqi Cheng, Ming Zhu

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961Z (2009) https://doi.org/10.1117/12.832984
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Infrared image offers the main advantage over visible image of being invariant to illumination changes for face recognition. In this paper, based on the introduction of main methods of linear subspace analysis, such as Principal Component Analysis (PCA) , Linear Discriminant Analysis(LDA) and Fast Independent Component Analysis (FastICA),the application of these methods to the recognition of infrared face images offered by OTCBVS workshop are investigated, and the advantages and disadvantages are compared. Experimental results show that the combination approach of PCA and LDA leads to better classification performance than single PCA approach or LDA approach, while the FastICA approach leads to the best classification performance with the improvement of nearly 5% compared with the combination approach.

Citation Download Citation

Wei Ge, Dawei Wang, Yuqi Cheng, and Ming Zhu "Infrared face recognition using linear subspace analysis", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961Z (30 October 2009); https://doi.org/10.1117/12.832984

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