DESIGN OF CROSSING NUMBER EXTRACTION FOR FINGERPRINT VERIFICATION
Identifying an individual is precisely based on her or his unique physiological attributes such as fingerprints, face, retina, and iris or behavioral attributes such as gait and signature characteristics known as biometrics. Biometrics is intrinsically more consistent and more skilled in distinguishing an endorsed person and a forged imposter than conventional token-based or knowledge-based methods. Fingerprint recognition is one of the most trustworthy and optimistic personal identification techniques in the midst of all biometrics technologies. Amongst all other distinctive physiological traits, fingerprint biometrics is the most extensively used and accredited by all. The fingerprint features are extracted by using crossing number method which focusing on extracting ridge ending and bifurcation point.
Design Steps:
Thinning-It is the process in which an image is converted to a 1-pixel thin image
Conditions for first sub-iteration
(a) 2 ≤ B(P1) ≤ 6
(b) A(P1)= 1
(C) P2*P4*P6 = 0
(d) P4*P6*P8 = 0
Conditions for second sub-iteration.
(a) 2 ≤ B(P1) ≤ 6
(b) A(P1)= 1
(c') P2*P4*P8 = 0
(d') P2*P6*P8 = 0
Do iterations until no pixel is deleted.
In the first sub iteration, all pixels satisfying first sub iteration conditions are removed.
In the second sub iteration, all pixels satisfying second sub iteration conditions are removed.
Minutiae Extraction-Crossing number method algorithm is used for extraction.
It assigns a number to a pixel according to its neighborhood pixel.
CN=1 and 3 are minutiae.
Reference Paper: Fingerprint Verification with Crossing Number Extraction and Orientation-Based Matching
Author’s Name: I. K. Virdaus, A. Mallak, S.-W. Lee1, G. Ha, and M. Kang
Source: International Conference on Next Generation Computing
Year:2017
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SIMULATION VIDEO DEMO
