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Overlapped Fingerprint Separation and Feature Enhancement using Gabor Filter
Abstract
Fingerprints are claimed to be both unique and
permanent, making it an ideal biometric trait for person identificationin .Fingerprint images generally contain either a single fingerprint or a set of non overlapped fingerprints. However, there are situations where several fingerprints overlap on top of each other. Overlapped images are mainly encountered in latent fingerprints lifted from crime scenes. Overlapping may also occur in lives can fingerprint images
when the surface of fingerprint sensors contains the residue of
fingerprints of previous users. In this paper, we propose a novel algorithm to separate overlapped fingerprints into component or individual fingerprints and evaluate it using both real overlapped latent fingerprints. The proposed method involves two basic assumptions which involve overlapped fingerprint is combination of
two fingers and two fingerprints has different orientation. It firstestimate the orientation field of the given image with overlappedfingerprints and then separates it into component orientation field using a relaxation labeling technique. Most Automatic Fingerprint Identification Systems (AFIS) use some form of image enhancement Once the Finger print was extracted Gabor filtered can be used toenhance the quality of extracted image.
Keywords
References
D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of
Fingerprint Recognition (Second Edition). Springer, 2009.
L. Hong, Y. Wan, and A. K. Jain, “Fingerprint image enhancement:
Algorithm and performance evaluation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777–789, 1998.
K. Jain and J. Feng, “Latent fingerprint matching,” IEEE Trans- actions
on Pattern Analysis and Machine Intelligence, vol. 99, no. PrePrints,
Neurotechnology. (2010) Verifinger 6.2. [Online]. Available:
http://www.neurotechnology.com/
S. Bramble and P. Fabrizi, “Observations on the effects of image
processing functions on fingermark data in the Fourier domain,” in
Proceedings of SPIE, vol. 2567, 1995, p. 138.
W. J. Watling, “Using the FFT in forensic digital image enhancement,”
Journal of Forensic Identification, vol. 43, no. 6, pp. 573–584, 1993.
H. Tang, W. Lu, C. Che, and K. Ng, “Gold nanoparticles and imaging
mass spectrometry: Double imaging of latent fingerprints,” Analytical
Chemistry, pp. 10 167–10 170.
X. Fan, D. Liang, and L. Zhao, “A scheme for separating overlapped
fingerprints based on partition mask,” Computer Engineering and
Applications, vol. 40, no. 2, pp. 80–81, 2004 (in Chinese).
R. Geng, Q. Lian, and M. Sun, “Fingerprint separation based on
morphological component analysis,” Computer Engineering and Applications,
vol. 44, no. 16, pp. 188–190, 2008 (in Chinese).
M. Singh, D. Singh, and P. Kalra, “Fingeprint separation: an application
of ICA,” in Proceedings of the SPIE, Mobile Multimedia/Image
Processing, Security, and Applications, vol. 6982, 2008, pp. 69 820L–1–
820L–11.
[11] A. K. Jain and J. Feng, “Latent palmprint matching,” IEEE
Transac- tions on Pattern Analysis and Machine Intelligence, vol. 31, no.
, pp. 1032–1047, 2009.
M. Pelillo and M. Refice, “Learning compatibility coefficients for
relaxation labeling processes,” IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 16, no. 9, pp. 933–945, 1994.
J. Zhou and J. Gu, “A model-based method for the computation of
fingerprints’ orientation field,” IEEE Transactions on Image Processing,
vol. 13, no. 6, pp. 821–835, 2004.
Y. Wang, J. Hu, and D. Phillips, “A fingerprint orientation model based
on 2D Fourier expansion (FOMFE) and its application to singular-point
detection and fingerprint indexing,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 29, no. 4, pp. 573– 585, 2007.
A. Rangarajan, “Self-annealing and self-annihilation: unifying deterministic
annealing and relaxation labeling,” Pattern Recognition, vol.
, no. 4, pp. 635–649, 2000.
J. Kittler and J. Illingworth, “Relaxation labelling algorithms–a reiew,”
Image and Vision Computing, vol. 3, no. 4, pp. 206–216, 1985.
A. Rosenfeld, R. A. Hummel, and S. W. Zucker, “Scene labeling by
relaxation operations,” IEEE Transactions on Systems, Man and
Cybernetics, vol. 6, no. 6, pp. 420–433, 1976.
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