Nonparametric maximum margin criterion for face recognition

Linear discriminant analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional image data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a new feature extraction method, nonparametric maximum margin criterion (NMMC), is proposed. NMMC finds the important discriminant directions without assuming the class densities belong to any particular parametric family, and it does not depend on the nonsingularity of the within-class scatter matrix. Our experimental results on the ATT and FERET face databases demonstrate that NMMC outperforms the existing variant LDA methods and the other state-of-art face recognition approaches.