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

Yanlai Li, Kuanquan Wang, Tao Li, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


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.
Original languageChinese (Simplified)
Pages (from-to)19-23
Number of pages5
JournalGaojishu Tongxin/Chinese High Technology Letters
Issue number12
Publication statusPublished - 1 Dec 2005


  • Modular neural network (MNN)
  • Palmprint recognition
  • Translation invariant Zernike moments

ASJC Scopus subject areas

  • General Engineering

Cite this