KR910010353A - 지문 인식 방법 - Google Patents

지문 인식 방법 Download PDF

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Publication number
KR910010353A
KR910010353A KR1019890017357A KR890017357A KR910010353A KR 910010353 A KR910010353 A KR 910010353A KR 1019890017357 A KR1019890017357 A KR 1019890017357A KR 890017357 A KR890017357 A KR 890017357A KR 910010353 A KR910010353 A KR 910010353A
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KR
South Korea
Prior art keywords
fingerprint
feature point
recognition method
input
same
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KR1019890017357A
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English (en)
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KR920002928B1 (ko
Inventor
김봉일
김문규
안영경
이재연
이상선
이황재
이윤석
김만희
장기혁
강남천
Original Assignee
박원희
한국과학기술연구원
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by 박원희, 한국과학기술연구원 filed Critical 박원희
Priority to KR1019890017357A priority Critical patent/KR920002928B1/ko
Priority to US07/618,299 priority patent/US5105467A/en
Priority to JP2325146A priority patent/JPH077458B2/ja
Publication of KR910010353A publication Critical patent/KR910010353A/ko
Application granted granted Critical
Publication of KR920002928B1 publication Critical patent/KR920002928B1/ko

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1365—Matching; Classification
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G07C9/30—Individual registration on entry or exit not involving the use of a pass
    • G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
    • G07C9/37—Individual registration on entry or exit not involving the use of a pass in combination with an identity check using biometric data, e.g. fingerprints, iris scans or voice recognition

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Computer Interaction (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Collating Specific Patterns (AREA)

Abstract

내용 없음

Description

지문 인식 방법
본 내용은 요부공개 건이므로 전문내용을 수록하지 않았음
제1도는 본 발명의 하드웨어 및 소프트웨어 전체 구성도.
제2도는 제1도 구성중 하드웨어 부분의 상세도.
제3도는 제2도 구성중 주처리장치의 상세도.

Claims (6)

  1. 등록과정을 통하여 등록된 지문과 입력부를 통하여 입력된 지문을 처리하여 인식, 판별함에 있어서, 등폭지문의 방향패턴과 입력 지문의 방향 패턴을 비교, 판별하는 1차 판별부에서는 방향차에 대한 2개의 임계값을 결정하여 두 지문 패턴간의 평균 방향차가 상위 임계값 이상일 경우에는 타인 지문으로, 하위 임계값 이하일 경우는 동일 지문으로 판별, 처리를 종료하며 2차 판별부에서는 평균방향차가 두 임계값 사이인 입력 자문을 대상으로 전처리(19), 편측윤곽선추출(20) 및 복원처리과정(22) 과정을 통하여 특징점을 추출한 후, 등록지문의 특징점과의 유사도를 비교함으로써 동일지문 여부의 판별을 특징으로 하는 복합 지문 인식 방법.
  2. 제1항에 있어서, 입력지문의 방향패턴과 등록 지문의 방향패턴을 중첩시키면서 평균방향차에 의한 좌표변환(17b)을, 실시하고, 평균방향차에 대하여 2개의 임계값을 적용함으로써 동일지문 여부를 판별함을 특징으로 하는 지문 인식 방법.
  3. 제1항에 있어서, 편측윤곽선추출(20)은 지문의 방향성에 따른 윤곽에 대한 선화상의 추출과정으로서, 좌상, 우상, 좌하, 우하의 4방향 성분중 각 대각선 쌍이 아닌 근접방향 성분에 의하여 추출, 합성되는 윤곽선을 말하며, 이러한 편측 윤곽선을 대상으로 하여 단점 및 분기점으로 구성된 특징점을 추출하여 동일 지문 여부를 판별함을 특징으로 하는 지문 인식 방법.
  4. 제1항 및 제3항에 있어서, 연결수와 흑화소수에 의하여 결정되는 특징점 추출과정은
    ⊙ 연결수 Cn=1X(i+1)-Xi1 (X=1…8. X9=X1)
    ⊙ 흑화소수 Bn=Xi (X=1…8)
    에서, Cn=2, Bn=1인 특징점을 결정하여 동일 지문 여부를 인식함을 특징으로 하는 지문 인식 방법.
  5. 제4항에 있어서, 지문의 각 특징점에 대한 특성으로 좌표(x,y), 종류(단점 및 분기점), 방향을 포함하도록하여 동일 지문 여부를 판별함을 특징으로 하는 지문 인식 방법.
  6. 제1항에 있어서, 의사 특징점의 제거는 특징점 추출(21)과정에서 1차 추출된 특징접중 최근접거리쌍을 제거하고, 남은 특징점을 대상으로 일정 거리내에 존재하면서 특징점 방향의 차가 180˚에 가까운 특징점쌍을 제거함으로써 특징점에 의한 동일 지문 여부를 판별함을 특징으로 하는 지문 인식 방법.
    ※ 참고사항 : 최초출원 내용에 의하여 공개하는 것임.
KR1019890017357A 1989-11-28 1989-11-28 지문 인식 방법 KR920002928B1 (ko)

Priority Applications (3)

Application Number Priority Date Filing Date Title
KR1019890017357A KR920002928B1 (ko) 1989-11-28 1989-11-28 지문 인식 방법
US07/618,299 US5105467A (en) 1989-11-28 1990-11-27 Method of fingerprint verification
JP2325146A JPH077458B2 (ja) 1989-11-28 1990-11-27 指紋照合方法

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
KR1019890017357A KR920002928B1 (ko) 1989-11-28 1989-11-28 지문 인식 방법

Publications (2)

Publication Number Publication Date
KR910010353A true KR910010353A (ko) 1991-06-29
KR920002928B1 KR920002928B1 (ko) 1992-04-10

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Country Status (3)

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US (1) US5105467A (ko)
JP (1) JPH077458B2 (ko)
KR (1) KR920002928B1 (ko)

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Also Published As

Publication number Publication date
JPH03231381A (ja) 1991-10-15
US5105467A (en) 1992-04-14
KR920002928B1 (ko) 1992-04-10
JPH077458B2 (ja) 1995-01-30

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