Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network

This paper proposes a novel and successful method for recognizing palmprint based on radial basis probabilistic neural network (RBPNN) proposed by us. The RBPNN is trained by the orthogonal least square (OLS) algorithm and its structure is optimized by the recursive OLS algorithm (ROLSA). The Hong Kong Polytechnic University (PolyU) palmprint database, which is pre-processed by a fast fixed-point algorithm for independent component analysis (FastICA), is exploited to test our approach. The experimental results show that the RBPNN achieves higher recognition rate and better classification efficiency than other usual classifiers.