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Biometric Recognition Performing in a Bioinspired System

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Abstract

In this study, we propose a set of biometric recognition experiments in similar conditions to real operating systems. This implies a jump from the usual laboratory conditions to a more real situation where the amount of variability between training and testing samples is large. We present experiments with face, hand-geometry, and signature recognition training a “universal classifier” able to decide if two input samples belong to the same person or not. During test, we recognize samples of a different database not used during classifier training. Training with the ORL face database and testing with the AR database provides a 5.1% error rate in verification operation, while training and testing with the same database yields 2.5%. For hand-geometry databases, we obtain 4.33 and 0.16% for different and same testing and training databases, respectively. For signature recognition, we obtain 1.36 and 4.14% for different and same testing and training databases, respectively. Our proposed system implies a very low computational cost to introduce/remove a user in the database, which is a crucial point for a real operation biometric system.

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Acknowledgments

This study has been supported by FEDER and MEC, TEC2006-13141-C03-02/TCM and COST-2102.

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  1. EUP Mataró, Avda. Puig i Cadafalch 101, 08303, Mataro, Barcelona, Spain

    Joan Fàbregas & Marcos Faundez-Zanuy

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  1. Joan Fàbregas
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  2. Marcos Faundez-Zanuy
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Correspondence to Marcos Faundez-Zanuy.

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Fàbregas, J., Faundez-Zanuy, M. Biometric Recognition Performing in a Bioinspired System. Cogn Comput 1, 257–267 (2009). https://doi.org/10.1007/s12559-009-9018-7

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