Fingerprint Quality Assessment
For a particular biometric to be effective, it should be universal: Every individual in the target population should possess the biometrics, and every acquisition from each individual should provide useful information for personal identity verification or recognition. In other words, everybody should have the biometrics and it should be easy to sample or acquire. In practice, adverse signal acquisition conditions and inconsistent presentations of the signal often result in unusable or nearly unusable biometrics signals (biometrics samples). This is confounded by the problem that the underlying individual biometrics signal can vary over time due, for example, to aging. Hence, poor quality of the actual machine sample of a biometrics constitutes the single most cause of poor accuracy performance of a biometrics system. Therefore, it is important to quantify the quality of the signal, either for seeking a better representation of the signal or for subjecting the poor signal to alternative methods of processin g (e.g., enhancement ). In this chapter,1 we explore a definition of the quality of fingerprint impressions and present detailed algorithms to measure image quality. The proposed quality measure has been developed with the use of human annotated images, and tested on a large number of fingerprints of different modes of fingerprint acquisition methods.
KeywordsQuality Assessment Biometric System Dominant Direction Directional Block Measure Image Quality
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- 1.Jain, A., L. Hong, S. Pankanti, and R. Bolle, Identity authentication using fingerprints, Proc. IEEE, 1365–1388, 1997.Google Scholar
- 2.Bolle, R. M., S. Pankanti, and Y-S. Yao, System and method for determining if a fingerprint image contains an image portion representing a partial fingerprint impression, U.S. Patent No. 6,005,963, Dec. 21, 1999.Google Scholar
- 3.Bolle, R. M., S. Pankanti, and Y-S. Yao, System and method for determining the quality of fingerprint images, U.S. Patent No. 5,963,656, Oct. 5, 1999.Google Scholar
- 4.Bolle, R. M., S. Pankanti, and Y-S. Yao, System and method for determining if a fingerprint image contains an image portion representing a dry fingerprint impression, U.S. Patent No. 5,995,640, Nov. 30, 1999.Google Scholar
- 5.Bolle, R. M., S. Pankanti, and Y-S. Yao, System and method for determining if a fingerprint image contains an image portion representing a smudged fingerprint impression, U.S. Patent No. 5,883,971, Mar. 16, 1999.Google Scholar
- 6.Levine, J. L., M. A. Schappert, N. K. Ratha, R. M. Bolle, S. Pankanti, R. S. Germain, and R. L. Garwin, System and method for distortion control in live-scan inkless fingerprint images, U.S. Patent No. US06064753, May 16, 2000.Google Scholar
- 8.Pankanti, S., N. Ratha, and R. Bolle, Structure in errors: A case study in fingerprints, ICPR 2002, Quebec City, Canada, 2002.Google Scholar
- 9.Ghosal, S., R. Udupa, S. Pankanti, and N. K. Ratha, Learning partitioned least squares filters for fingerprint enhancement, Workshop on the Application of Computer Vision (WACV2000) Dec. 4–6, 2000, Palm Springs, CA.Google Scholar
- 10.Yao, Y.S., S. Pankanti, N. Haas, N. Ratha, and R. Bolle, Quantifying quality: A case study in fingerprints, IEEE AutoID Conference, March 14–15, 2002.Google Scholar
- 12.Mehtre, B. M., Fingerprint Image Analysis for Automatic Identification, Machine Vision and Applications, Springer-Verlag, Vol. 6, 1993, pp. 124–139.Google Scholar
- 13.Maio, D., D. Maltoni, R. Capelli, J.L. Wayman, and A.K. Jain. FVC2000: Fingerprint verification competition. Technical report, Univ. of Bologna, Sept. 2000.Google Scholar
- 14.Watson, C. I., NIST Special Database 9, Mated Fingerprint Card Pairs, National Institute of Standards and Technology, 1993.Google Scholar
- 15.Ratha, N., K. Karu, S. Chen, and A. K. Jain, A real-time matching system for large fingerprint database, IEEE Trans. on PAMI, 18(8):799–813, 1996.Google Scholar
- 16.Bolle, R., N. Ratha, and S. Pankanti, Evaluating authentication systems using bootstrap confidence intervals, Proc. 1999 IEEE Workshop on Automatic Identification Advanced Technologies (Morristown, NJ), Oct. 28–29, 1999, pp. 9–13.Google Scholar
- 17.Ratha, Nalini K., and Ruud M. Bolle, Fingerprint quality assessment, Proc. of 4th Conference on Computer Vision, Jan 8–11, 2000, Taipei, pp. 819–823.Google Scholar
- 18.Lim, E., X. Jiang, and W. Yau, Fingerprint quality and validity analysis, IEEE Proc. ICIP, Rochester, NY, 2002, pp. I–469–I–472.Google Scholar
- 19.Senior, A. W., and R. Bolle, Improved fingerprint matching by distortion removal, IEICE Trans. Special Issue on Biometrics, 2001.Google Scholar
- 20.Phillips, P. J., Grother, P., Michaels, R., Blackburn, D. M., Elham, T., Bone, J. M., FRVT200: Face Recognition vendor test, http://www.frvt.org, 2003.