인공지능 지문인식보드를 이용한 개인정보 조회시스템에 관한 연구 = A study on the Personal Information Certification System using Atificial Intelligence Fingerprint Recognition Board

다국어 초록 (Multilingual Abstract)

This paper extracted characteristic points(end points,
divergent points) and the central point of fingerprints by applying the neural network algorithm. With the back-propagation algorithm of neural network algorithms, ambiguity of the central point was minimized and thus the central point was extracted. Centered on the central point, lengths between the central point and end points(and divergent points) were measured respectively.
During the pre-processing procedure, Butterworth Low-Pass Filter was used to remove noises of fingerprint image. It was learned that the frequency domain was more effective in processing fingerprint images than the space domain. So Butterworth Low-Pass Filter, which processes fingerprint images in the frequency domain, was adopted in this research. Smoothing, binarization, sessionization, and histograms equalization were extracted. Then, the data of orientation were used as input data for the neural network in order to extract the central point of fingerprint.
TI's DSP(TMS320VC5509) was used as the main board of the fingerprint recognition system, and ATMEL's AVR(ATmega16L) was used as the control board. MFC, which was chosen as personal information inquiry system, manages and displays detailed personal data on PC screen by connecting the fingerprint recognition system
through serial communication. In the personal information inquiry system, data sources are made and registered at ODBC Data Sources manager before making programs.
Fifty fingerprints of 10 people(five fingerprints of each person) were used to check the recognition rate. Verification results were retrieved by comparing one fingerprint of a person with forty-five fingerprints of the other persons(FAR(0.1)) and one fingerprint of a person with the rest four images of that person(FRR(4.5)).

목차 (Table of Contents)

• 제 1 장 서론 = 1
• 제 2 장 신경회로망 = 4
• 2.1 신경회로망의 기본구조 = 4
• 2.2 신경회로망의 모델 = 5
• 2.3 역전파 알고리즘 (Backpropagation(BP))신경망 = 7
• 제 3 장 지문인식 알고리즘 = 10
• 3.1 천처리 과정 = 11
• 3.1.1 Hybrid 미디언 필터링에 의한 영상 잡음 제거 = 12
• 3.1.2 FFT를 이용한 버터워스 저주파 통과필터링에 의한 잡음 제거 = 14
• 3.1.3 히스토그램 평활화 = 17
• 3.1.4 이진화 = 19
• 3.1.5 세선화 = 20
• 3.2 영상의 특징 추출 = 23
• 3.2.1 지문 영상의 특징점 추출 과정 = 23
• 3.2.2 지문 영상의 중심점 추출 과정 = 25
• 3.3 지문의 정합 = 34
• 제 4 장 시스템 설계 = 38
• 4.1 메인 모듈 = 39
• 4.2 컨트롤 모듈 = 46
• 4.3 개인정보 조회프로그램 = 49
• 제 5 장 실험 결과 = 52
• 제 6 장 결론 = 56
• 참고문헌 = 57