Learning Global-Local Distance Metrics for Signature-Based Biometric Cryptosystems

George Sekladious, Robert Sabourin and Eric Granger
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George Sekladious: Ecole de technologies superieure, University du Quebec, Canada
Robert Sabourin: Ecole de technologies superieure, University du Quebec, Canada
Eric Granger: Ecole de technologies superieure, University du Quebec, Canada

Biostatistics and Biometrics Open Access Journal, 2017, vol. 3, issue 2, 49-63

Abstract: Biometric traits, such as fingerprints, faces and signatures have been employed in bio-cryptosystems to secure cryptographic keys within digital security schemes. Reliable implementations of these systems employ error correction codes formulated as simple distance thresholds, although they may not e actively model the complex variability of behavioral biometrics like signatures. In this paper, a Global-Local Distance Metric (GLDM) framework is proposed to learn cost active distance metrics, which reduce within-class variability and augment between class variability, such that simple error correction thresholds of bio-cryptosystems provide high classification accuracy. First, a large number of samples from a development dataset are used to train a global distance metric that differentiates with in class from between-class samples of the population.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:3:y:2017:i:2:p:49-63

DOI: 10.19080/BBOAJ.2017.03.555610

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