Research of palmprint identification method using Zernike moment and neural network

Having thoroughly researched the existing palm print identification technology, in this paper, we propose a hierarchical multi-feature scheme to facilitate coarse-to-fine matching for efficient and effective palm print recognition. In our approach, first of all, we define two levels of feature: geometry feature based on distance (level-1 feature) and texture feature based on Zernike moment (level- 2 feature). Then we adopt two different kinds of neural network for different features, and then combine the two into one recognition system effectively. Finally, the experimental results demonstrate the feasibility and efficiency of the proposed system.