OPTIMAL SUBSPACE ANALYSIS FOR FACE RECOGNITION
Haitao Zhao, Pong Chi Yuen, Jingyu Yang · International Journal of Pattern Recognition and Artificial Intelligence · 2005
Fisher Linear Discriminant Analysis (LDA) has been successfully used as a data discriminantion technique for face recognition. This paper has developed a novel subspace approach in determining the optimal projection. This algorithm effectively solves the small sample size problem and eliminates the possibility of losing discriminative information. Through the theoretical derivation, we compared our method with the typical PCA-based LDA methods, and also showed the relationship between our new method and perturbation-based method. The feasibility of the new algorithm has been demonstrated by comprehensive evaluation and comparison experiments with existing LDA-based methods.