title: Fingerprint recognition with identical twin fingerprints.
abstract: Fingerprint recognition with identical twins is a challenging task due to the closest genetics-based relationship existing in the identical twins. Several pioneers have analyzed the similarity between twins' fingerprints. In this work we continue to investigate the topic of the similarity of identical twin fingerprints. 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. 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. (2) Six impressions per finger were captured, rather than just one impression, which makes the genuine distribution of matching scores more realistic. (3) A larger sample (83 pairs) was collected. (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. (5) A novel analysis, which aims at showing which finger from identical twins has higher probability of having same fingerprint type, has been conducted. Our results showed that: (a) A state-of-the-art automatic fingerprint verification system can distinguish identical twins without drastic degradation in performance. (b) The chance that the fingerprints have the same type from identical twins is 0.7440, comparing to 0.3215 from non-identical twins. (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. (d) For each of four fingers of identical twins, the probability of having same fingerprint type is similar.
authors.affiliation: Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
authors.forename: Xunqiang
authors.initials: X
authors.lastname: Tao
authors.forename: Xinjian
authors.initials: X
authors.lastname: Chen
authors.forename: Xin
authors.initials: X
authors.lastname: Yang
authors.forename: Jie
authors.initials: J
authors.lastname: Tian
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D003878:Dermatoglyphics; D005385:Fingers / Q000033:anatomy & histology; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D014430:Twins, Monozygotic