Research and Evaluation of Multispectral Feature Selection and Fusion Model for Palmprint Recognition

Abstract

Palmprint is widely used in personal identification for an accurate and robust recognition. Multispectral palmprint images capture under different illumination, including Red, Green, Blue and Infrared maybe contribute to the recognition results. However, the evaluation of selection and fusion of how this different spectral images can contribute to improve the robustness of the recognition system is imperative. In this paper, a novel wavelet-based multispectral fusion strategy is presented firstly to obtain the fused images; then block singular value decomposition (B-SVD) is applied for feature extraction; Finally back propagation (BP) neural network method is adopted for authentication. The proposed algorithm is evaluated on PolyU database which contains palmprint images from 500 individuals from four independent frequent band. The obtained results show robustness of our multispectral palmprint image fusion and selection model in comparison with the single spectral palmprint image that presented in the literature.