화질 추정과 특징점 분류를 이용한 Delaunay 삼각화 기반의 지문 정합 알고리즘

Delaunay Triangulation based Fingerprint Matching Algorithm using Quality Estimation and Minutiae Classification

  • 투고 : 2009.11.11
  • 심사 : 2010.04.13
  • 발행 : 2010.04.30

초록

Delaunay 삼각화는 회전과 평행 이동에 강건하여 지문 인식에 적합하다. 그러나 특징점이 소실되거나 추가로 추출되는 경우 지문 인식 성능이 저하되고 계산 복잡도가 증가하게 된다. 본 논문에서는 지문 영상의 지역적 화질 척도 분석을 통해 저화질 영역을 설정하고, 추출된 특징점을 4개의 그룹으로 분류하여 정합과정에 이용함으로써 인식 성능을 개선하고 계산 복잡도를 감소시켰다. 정합 후보 영역에서의 저화질 영역 제거와 특징점 분류에 대한 실험을 통해 성능의 저하 없이 정합 소요 시간이 감소되는 것을 확인하였고, NIST에서 제공하는 정합 알고리즘 (BOZORTH3)보다 정합 성능이 향상되었음을 확인하였다.

Delaunay triangulation is suitable for fingerprint matching because of its robustness to rotation and translation. However, missing and spurious minutiae degrade the performance and computational efficiency. In this paper, we propose a method of combining local quality assessment and 4-category minutiae classification to improve accuracy and decrease computational complexity in matching process. Experimental results suggest that removing low quality areas from matching candidate areas and classifying minutiae improve computational efficiency without degrading performance. The results proved that the proposed algorithm outperforms the matching algorithm (BOZORTH3) provided by NIST.

키워드

참고문헌

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