Browse > Article
http://dx.doi.org/10.13089/JKIISC.2007.17.6.19

Measurement of Fingerprint Image Quality using Hybrid Segmentation method  

Park, Noh-Jun (Korea Information Security Agency)
Jang, Ji-Hyeon (School of Information & Communication Engineering, Inha University)
Kim, Hak-Il (School of Information & Communication Engineering, Inha University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.17, no.6, 2007 , pp. 19-28 More about this Journal
Abstract
The purpose of this paper is to present a new measure for fingerprint image quality assessment that has a considerable effect on evaluation of fingerprint databases. This paper introduces a hybrid segmentation method for measuring an image quality and evaluates the experimental results using various fingerprint databases. This study compares the performance of the proposed hybrid segmentation using variance and coherence of fingerprints against the NIST's NFIQ program. Although NFIQ is a most widely used tool, it classifies the image quality into 5 levels. However, the proposed hybrid method is developed to be conformant to the ISO standards and accordant to human visual perception. The experimental results demonstrate that the hybrid method is able to produce finer quality measures.
Keywords
Image quality; Fingerprint database; Segmentation; Coherence; Variance;
Citations & Related Records
연도 인용수 순위
  • Reference
1 N. Nill and B.H. Bouzas. 'Objective image quality measure derived from digital image power spectra.' Optical Enginering, Vol. 31 No, 4, pp. 813-825, April 1992   DOI
2 R.M. Bolle et al. 'System and methods for determining the quality of fingerprint images', United States patent number US596356, 1999
3 N.K. Ratha and R.M. Bolle. 'Fingerprint image quality estimation', IBM computer science research report RC21622, 1999
4 박노준 , '이기종 지문센서의 호환을 위한 Segmentation & Orientation 알고리즘 개발', 인하대학교 대학원 석사학위 논문
5 http://fingerprint.nist.gov/NFIS/index.html
6 L.L. Shen, A. Kot and W.M Koo. 'Quality measures of fingerprint images.'3rd international conference AVBPA 2001, pp. 182-271, June 2001
7 A.M. Bazen and S.H. Gerez, 'Directional Field Computation for Fingerprints Based on the Principal Component Analysis of Local Gradients,' Proc.ProRISC2000 Workshop on Circuits, Systems and Signal Processing, pp. 215-222 , 2000
8 C.L. Wilson, J.L Blue and O.M. Omidvar, 'Training Dynamics and Neural Network Performance', Neural Networks, Vol. 10, No. 5, pp. 907-923, 1997   DOI   ScienceOn
9 N. Ratha, S. Chen, and A. Jain, 'Adaptive flow orientation based feature extraction in fingerprint images,' Pattern Recognition, vol.28, no.11, pp. 1657-1672, 1995   DOI   ScienceOn