Fisher Discriminant Analysis with L1-Norm for Robust Palmprint Recognition

In this paper, a linear discriminant analysis method with L1 norm (LDA-L1) for palmprint recognition is proposed. The traditional linear discriminant analysis method based on L2 norm is very sensitive to outliers, but the L1 specification can overcome this problem very well. In the LDA-L1 method, a series of projection vectors are obtained by the iterative method, which can maximize the inter class dispersion and minimize the L1 norm based on within class dispersion. We tested the performance of our approach in the PolyU palmprint database. The experimental results show that LDA-L1 has robustness to outliers.