Abstract
Problem statement: As the importance of automatic personal identificat ion applications increases, biometrics particularly fin gerprint identification is the most consistent and greatly acknowledged technique. A very important st ep in automatic fingerprint recognition system is to extract the minutiae points from the i nput fingerprint images automatically and quickly. Approach: Fingerprints from the database FVC2002 (DB1-a) is used for experimental purpose. The minutiae points from 100 fingerprints were detected. It is proposed to use Minutiae Detection using Crossing Numbers (MDCN) and Minutiae Detection using Midpoint Ridge Contour Method (MDMRCM). Finally the performance of minutiae extraction algorithms using the number of minutiae detected in both the cases w ere analysed. Results: The result shows that the avearge performance of MDCN method for minutiae points detection is 88% and for MDMRCM method is 92%. Conclusion: The performance of MDMRCM is better than MDCN method. MDMRCM method extract more minutiae points than MDCN method. It consumes lesser time to get the output and the false minutiae points were not d etected. And hence MDMRCM method is considered to be a superior than MDCN method.
Highlights
Biometrics and fingerprints: Personal identification are usually divided into three types as shown in Table 1, by what one owns, by something you know or by physiological or behavioural characteristics
Automatic Fingerprint Identification System technology, yet there is no significant difference in (AFIS): For contemporary applications the fingerprint minutiae count based on the force levels of the identification/verification process is undertaken capacitance sensor
It involves five operations; (I) orientation estimation, with the purpose to estimate local ridge directions (II) ridge detection, which separate ridges from the valleys by using the orientation estimation resulting in a binary image (III) thinning algorithm/skeletonization, giving the ridges a width of 1 pixel, (IV) minutiae detection, identifying ridge pixels with three ridge pixel neighbours as ridge bifurcations and those with one ridge pixel neighbour as ridge endings and (V) post processing, which removes spurious minutiae
Summary
Biometrics and fingerprints: Personal identification are usually divided into three types as shown in Table 1, by what one owns (e.g., a credit card or keys), by something you know (e.g., a password or a PIN code) or by physiological or behavioural characteristics. The last method is referred to as biometrics and the six most commonly used features include face, voice, iris, signature, hand geometry and fingerprint identification (Maltoni et al, 2009). It has been established and is commonly known, that everyone has a unique fingerprint (Yager and Amin, 2004) which do not change over time. He introduced five different types of fingerprints; right loop, left loop, whorl, arch, tented arch.
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