A novel ant colony optimization algorithm for large-distorted fingerprint matching

  • Authors:

  • Kai Cao;Xin Yang;Xinjian Chen;Yali Zang;Jimin Liang;Jie Tian

  • Affiliations:

  • Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, China;Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, MD 20892, USA;Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, China;Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an 710071, China and Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2012

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Abstract

Large distortion may be introduced by non-orthogonal finger pressure and 3D-2D mapping during the process of fingerprint capturing. Furthermore, large variations in resolution and geometric distortion may exist among the fingerprint images acquired from different types of sensors. This distortion greatly challenges the traditional minutiae-based fingerprint matching algorithms. In this paper, we propose a novel ant colony optimization algorithm to establish minutiae correspondences in large-distorted fingerprints. First, minutiae similarity is measured by local features, and an assignment graph is constructed by local search. Then, the minutiae correspondences are established by a pseudo-greedy rule and local propagation, and the pheromone matrix is updated by the local and global update rules. Finally, the minutiae correspondences that maximize the matching score are selected as the matching result. To compensate resolution difference of fingerprint images captured from disparate sensors, a common resolution method is adopted. The proposed method is tested on FVC2004 DB1 and a FINGERPASS cross-matching database established by our lab. The experimental results demonstrate that the proposed algorithm can effectively improve the performance of large-distorted fingerprint matching, especially for those fingerprint images acquired from different modes of acquisition.