Infrared face recognition using linear subspace analysis
Wei Ge, Dawei Wang, Yuqi Cheng, Ming Bo Zhu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
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.