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Finger-Knuckle-Print Recognition Using Local Orientation Feature Based on Steerable Filter

  • Zichao Li
  • Kuanquan Wang
  • Wangmeng Zuo
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

Automatic personal identification based on finger-knuckle-print (FKP) has been considered as a promising technology in biometrics family in recent years. Previous work indicates that local orientation analysis supplies an efficient framework for FKP representation. In this paper, we propose a novel FKP recognition method using the Adaptive Steerable Orientation Coding (ASOC). High order steerable filters are first employed to extract the continuous orientation feature map, then we use multilevel histogram thresholding method to quantize the feature map adaptively and the discrete orientations are used for coding a FKP image. Furthermore, we measure the similarity between two coded FKP images by designing an effective angular matching function. Experimental results on the PolyU FKP database demonstrate the accuracy of the proposed method.

Keywords

Biometrics finger-knuckle-print steerable filter local orientation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zichao Li
    • 1
  • Kuanquan Wang
    • 1
  • Wangmeng Zuo
    • 1
  1. 1.Biocomputing Research Center, School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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