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A Computational Discriminability Analysis on Twin Fingerprints

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 5718)

Abstract

Sharing similar genetic traits makes the investigation of twins an important study in forensics and biometrics. Fingerprints are one of the most commonly found types of forensic evidence. The similarity between twins’ prints is critical establish to the reliability of fingerprint identification. We present a quantitative analysis of the discriminability of twin fingerprints on a new data set (227 pairs of identical twins and fraternal twins) recently collected from a twin population using both level 1 and level 2 features. Although the patterns of minutiae among twins are more similar than in the general population, the similarity of fingerprints of twins is significantly different from that between genuine prints of the same finger. Twins fingerprints are discriminable with a 1.5%~1.7% higher EER than non-twins. And identical twins can be distinguished by examine fingerprint with a slightly higher error rate than fraternal twins.

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Liu, Y., Srihari, S.N. (2009). A Computational Discriminability Analysis on Twin Fingerprints. In: Geradts, Z.J.M.H., Franke, K.Y., Veenman, C.J. (eds) Computational Forensics. IWCF 2009. Lecture Notes in Computer Science, vol 5718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03521-0_5

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  • DOI: https://doi.org/10.1007/978-3-642-03521-0_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03520-3

  • Online ISBN: 978-3-642-03521-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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