Embedded palmprint recognition system using OMAP 3530

Linlin Shen, Shipei Wu, Songhao Zheng, Zhen Ji

Research output: Journal PublicationArticlepeer-review

18 Citations (Scopus)


We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance.

Original languageEnglish
Pages (from-to)1482-1493
Number of pages12
Issue number2
Publication statusPublished - Feb 2012
Externally publishedYes


  • Embedded system
  • Gabor wavelet
  • Palmprint recognition

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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