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A Study on the Performance Improvement of Incomplete Fingerprint Classification using an Adaptive Core Block Based on Markov Models

마코프 모델 기반 적응적 중심블록을 이용한 불완전한 지문의 분류 성능 향상에 관한 연구

  • 정혜욱 (성균관대학교 컴퓨터공학과) ;
  • 이지형 (성균관대학교 컴퓨터공학과)
  • Received : 2012.08.27
  • Accepted : 2012.09.25
  • Published : 2012.11.01

Abstract

We propose a novel approach to classify fingerprints using the extracted adaptive core block for improving classification performance of incomplete fingerprints in this paper. We compute representative directions from fingerprint images by the block unit and learn horizontal and vertical Markov models by deciding the center position of a fingerprint image based on the expert knowledge. The center block of a test image is the block has the highest probability after comparing the Markov model with $11{\times}11$ blocks. The proposed approach can effectively classify incomplete fingerprints using the optimal center block.

Acknowledgement

Grant : 스마트TV 2.0 소프트웨어 플랫폼

Supported by : 한국연구재단, 한국산업기술평가관리원

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