![]() The performance of biometric verification systems highly depends on the distinctiveness of the biometric characteristics. Any physical or behavioral characteristics which can be used as verification to recognize a person must satisfy the following requirements 0: (i) universality (everyone possesses the characteristic) (ii) permanence (the characteristic remains invariant over a life time) (iii) collectability (the characteristic is easy to capture) and (iv) distinctiveness (the characteristic is different for everyone). During the past few decades, a number of verification systems based on different biometric characteristics have been proposed. Finally, the biometric characteristics of identified person cannot be lost or forged. Second, identification based on biometric characteristics avoids the need to carry a card or remember a password. First, the person to be identified is required to be physically present at the point of identification to provide his or her biometric traits. ![]() These methods have advantages over traditional token based identification approaches using a physical key or access card, and over knowledge based identification approaches that use a password for various reasons. (d) For each of four fingers of identical twins, the probability of having same fingerprint type is similar.īiometrics refers to the automatic identification of a person based on his or her physiological or behavioral characteristics. (c) For the corresponding fingers of identical twins which have same fingerprint type, the probability distribution of five major fingerprint types is similar to the probability distribution for all the fingers' fingerprint type. (b) The chance that the fingerprints have the same type from identical twins is 0.7440, comparing to 0.3215 from non-identical twins. ![]() Our results showed that: (a) A state-of-the-art automatic fingerprint verification system can distinguish identical twins without drastic degradation in performance. (5) A novel analysis, which aims at showing which finger from identical twins has higher probability of having same fingerprint type, has been conducted. (4) A novel statistical analysis, which aims at showing the probability distribution of the fingerprint types for the corresponding fingers of identical twins which have same fingerprint type, has been conducted. (3) A larger sample (83 pairs) was collected. (2) Six impressions per finger were captured, rather than just one impression, which makes the genuine distribution of matching scores more realistic. Compared to the previous work, our contributions are summarized as follows: (1) Two state-of-the-art fingerprint identification methods: P071 and VeriFinger 6.1 were used, rather than one fingerprint identification method in previous studies. Our study was tested based on a large identical twin fingerprint database that contains 83 twin pairs, 4 fingers per individual and six impressions per finger: 3984 (83*2*4*6) images. In this work we continue to investigate the topic of the similarity of identical twin fingerprints. Several pioneers have analyzed the similarity between twins' fingerprints. Fingerprint recognition with identical twins is a challenging task due to the closest genetics-based relationship existing in the identical twins. ![]()
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