Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30695
Title: Palmprint recognition based on translation invariant Zernike moments and modular neural network
Authors: Li, Y
Wang, K
Li, T
Zhang, D 
Keywords: Modular neural network (MNN)
Palmprint recognition
Translation invariant Zernike moments
Issue Date: 2005
Publisher: 中国学术期刊(光盘版)电子杂志社
Source: 高技术通讯 (High technology letters), 2005, v. 15, no. 12, p. 19-23 How to cite?
Journal: 高技术通讯 (High technology letters) 
Abstract: 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 Polyu_PalmprintDB 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.
URI: http://hdl.handle.net/10397/30695
ISSN: 1002-0470
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