Palmprint recognition based on translation invariant Zernike moments and modular neural network

Authors:
Yanlai Li;Kuanquan Wang;David Zhang
Affiliations:
Biometrics Research Center, Dept. of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China;Biometrics Research Center, Dept. of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

This paper introduces a new approach, TIZMs & MNN, for palmprint recognition. It uses translation invariant Zernike moments (TIZMs) as palm features, and a modular neural network (MNN) as classifier. Translation invariance is added to the general Zernike moments which have very good property of rotation invariance. A fast algorithm for computing the TIZMs is adopted to improve the computation speed. The pattern set is set up by eightorder TIZMs. Because palmprint recognition is a large-scale multi-class task, it is quite difficult for a single multilayer perceptrons to be competent. A modular neural network is presented to act the classifier, which can decompose the palmprint recognition task into a series of smaller and simpler two-class subproblems. Simulations have been done on the Polyu_PalmprintDB database. Experimental results demonstrate that higher identification rate and recognition rate are achieved by the proposed method in contrast with the straight-line segments (SLS) based method [2].