A Study on Fingerprint Hash Code Generation Using Euclidean Distance for Identifying a User
International Journal of Management, Technology, and Social Sciences (IJMTS), 2(2), 116-126.
11 Pages Posted: 5 Jan 2018
Date Written: December 28, 2017
Biometrics innovation has ended up being a precise and proficient response to the security issue. Biometrics is a developing field of research as of late and has been dedicated to the distinguishing proof or authentication of people utilizing one or multiple inherent physical or behavioural characteristics. The unique fingerprint traits of a man are exceptionally exact and are special to a person. Authentication frameworks in light of unique fingerprints have demonstrated to create low false acceptance rate and false rejection rate, alongside other favourable circumstances like simple and easy usage strategy. But the modern study reveals that fingerprint is not so secured like secured passwords which consist of alphanumeric characters, number and special characters. Fingerprints are left at crime places, on materials or at the door which is usually class of latent fingerprints. We cannot keep fingerprint as secure like rigid passwords. In this paper, we discuss fingerprint image Hash code generation based on the Euclidean distance calculated on the binary image. Euclidean distance on a binary image is the distance from every pixel to the nearest neighbour pixel which is having bit value one. Hashcode alone not sufficient for Verification or Authentication purpose, but can work along with Multifactor security model or it is half secured. To implement Hash code generation we use MATLAB2015a. This study shows how fingerprints Hash code uniquely identifies a user or acts as index-key or identity-key.
Keywords: Fingerprint image, Fingerprint hashcode, Authentication, Multifactor authentication model, Euclidean distance
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