On the dissimilarity representation and prototype selection for signature-based bio-cryptographic systems

Authors:
George S. Eskander;Robert Sabourin;Eric Granger
Affiliations:
Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle, Ecole de Technologie Supérieure, Université du Québec, Montréal, QC, Canada;Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle, Ecole de Technologie Supérieure, Université du Québec, Montréal, QC, Canada;Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle, Ecole de Technologie Supérieure, Université du Québec, Montréal, QC, Canada
Venue:
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Year:
2013

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Abstract

Robust bio-cryptographic schemes employ encoding methods where a short message is extracted from biometric samples to encode cryptographic keys. This approach implies design limitations: 1) the encoding message should be concise and discriminative, and 2) a dissimilarity threshold must provide a good compromise between false rejection and acceptance rates. In this paper, the dissimilarity representation approach is employed to tackle these limitations, with the offline signature images are employed as biometrics. The signature images are represented as vectors in a high dimensional feature space, and is projected on an intermediate space, where pairwise feature distances are computed. Boosting feature selection is employed to provide a compact space where intra-personal distances are minimized and the inter-personal distances are maximized. Finally, the resulting representation is projected on the dissimilarity space to select the most discriminative prototypes for encoding, and to optimize the dissimilarity threshold. Simulation results on the Brazilian signature DB show the viability of the proposed approach. Employing the dissimilarity representation approach increases the encoding message discriminative power (the area under the ROC curve grows by about 47%). Prototype selection with threshold optimization increases the decoding accuracy (the Average Error Rate AER grows by about 34%).