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

Author

Listed:
  • 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)

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.

Suggested Citation

  • George Sekladious & Robert Sabourin & Eric Granger, 2017. "Learning Global-Local Distance Metrics for Signature-Based Biometric Cryptosystems," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 3(2), pages 49-63, October.
    • Handle: RePEc:adp:jbboaj:v:3:y:2017:i:2:p:49-63
    DOI: 10.19080/BBOAJ.2017.03.555610
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