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Proceedings Paper

Probabilistic face authentication using hidden Markov models
Author(s): Manuele Bicego; Enrico Grosso; Massimo Tistarelli
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Paper Abstract

In this paper a novel approach for face authentication is proposed, based on the Hidden Markov Model (HMM) tool. While this technique has been largely and successfully employed in face recognition systems, its use in the authentication context has poorly been investigated. The method proposed in this paper extracts from the image a sequence of partially overlapped images, from which different kinds of simple and quickly computable features are extracted. The face template is obtained by modelling the sequence with a continuous Gaussian Hidden Markov Model. Given an unknown subject, the authentication phase is carried out by thresholding the likelihood of the given face with respect to the HMM template. The proposed approach has been thoroughly tested on the ORL database, also applying different parameters' configurations. A comparison with two other state-of-the-art approaches is also reported. The results obtained are really promising, showing the wide applicability of the Hidden Markov Models methodology.

Paper Details

Date Published: 28 March 2005
PDF: 8 pages
Proc. SPIE 5779, Biometric Technology for Human Identification II, (28 March 2005); doi: 10.1117/12.603286
Show Author Affiliations
Manuele Bicego, Univ. degli Studi di Sassari (Italy)
Enrico Grosso, Univ. degli Studi di Sassari (Italy)
Massimo Tistarelli, Univ. degli Studi di Sassari (Italy)


Published in SPIE Proceedings Vol. 5779:
Biometric Technology for Human Identification II
Anil K. Jain; Nalini K. Ratha, Editor(s)

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