Abstract

This work presents a novel Lattice Computing (LC) approach for person identification based on biometric data including fingerprints. In particular, this work, first, engages conventional techniques for fingerprint image preprocessing towards inducing distributions of fingerprint minutiae, moreover an induced distribution is represented by an Intervals’ Number (IN). Second, it employs a kNN classifier in a metric lattice of INs. The effectiveness of the proposed approach is demonstrated comparatively by computational experiments using a software developed for the needs of this work. The far reaching potential of the proposed approach is discussed.

Citation

T. Pachidis, V.G. Kaburlasos, “Person identification based on lattice computing k-nearest-neighbor fingerprint classification”, 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2012), San Sebastián, Spain, 10-12 September 2012, Advances in Knowledge-Based and Intelligent Information and Engineering Systems. IOS Press, 2012, Manuel Graña, Carlos Toro, Jorge Posada, R. J. Howlett, L. C. Jain (Eds.), pp. 1720-1729.

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