DOI QR코드

DOI QR Code

영상처리 기법을 통한 RBFNN 패턴 분류기 기반 개선된 지문인식 시스템 설계

배종수;오성권;김현기
Bae, Jong-Soo;Oh, Sung-Kwun;Kim, Hyun-Ki

  • 투고 : 2016.03.02
  • 심사 : 2016.04.07
  • 발행 : 2016.06.01

초록

In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.

키워드

RBFNN;fingerprint;identification;pattern classifier

참고문헌

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과제정보

연구 과제번호 : Centre for U-city Security & Surveillance Technology

연구 과제 주관 기관 : National Research Foundation of Korea(NRF), GRRC