Practical Face Recognition via Factor Analysis

Considering the variation of illumination, expression and pose, a new face recognition algorithm is proposed based on factor analysis and data mining. The consistence of factor analysis model based on content and style with linear discriminant analysis in face recognition is analyzed. In order to improve the robustness of this method, two-factor analysis of variance and additive model is proposed to reduce the impact of style information on face observed feature. Experimental results show that this method has higher and more stable performance than Fisherface method. Especially, when the fisherface method performance is bad under complex environments while this method demonstrates better performance.