A Novel LDA Algorithm Based on Approximate Error Probability with Application to Face Recognition

Extracting proper features is crucial to the performance of a pattern recognition system. Popular feature extraction techniques like principal component analysis (PCA), Fisher linear discriminant analysis (FLD), and independent component analysis (ICA) extract features that are not directly related to the classification accuracy. In this paper, we propose a new linear discriminant analysis algorithm (LDA) whose criterion function is based on the probability of classification error. The efficiency of this novel algorithm is demonstrated by application to face recognition problems.