基于平移不变Zernike矩和模块化神经网络的掌纹识别方法

在对掌纹原始图像进行去噪、分割等预处理之后，利用平移不变的Zernike矩特征矢量（TIZMs）作为掌纹特征建立特征库，根据已知分类信息建立样本集。并将问题分解为多个小规模的两类问题，然后采用模块化神经网络（MNN）作为分类器进行掌纹识别。对香港理工大学的Polyu PalmprintDB数据库中的3200个掌纹进行实验，在响应时间和识别精度等方面获得了很好的结果。

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

This paper introduces a new approach for palmprint recognition,using 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 a good property of rotation invariance.The pattern set is set up by eight-order TIZMs with 25 dimensions.A modular neural network is presented in order to decompose the palmprint recognition task into a series of smaller and simpler two-class sub-problems.Simulations have been done on the PolyuPalmprintDB database,which is composed of 3200 palmprints(10 palmprints/person).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 and the Fuzzy Directional Element Energy Feature(FDEEF) method.