A two stage neural network-based personal identification system using handprint

With the increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decades. Handprint identification, as an emerging biometric identification technology, is receiving more and more attention in both research and practical applications as time goes by. In this paper, a novel approach for handprint identification is proposed. Firstly, region of interest is segmented through hand's key points localization, then the Gabor filtering and Zernike moments methods are used to extract the palmprint features. A two stage neural network structure is employed to measure the degree of similarity in the identification stage. The experimental results demonstrate that the proposed approach is effective and feasible.