Abstract
This paper presents a novel approach to estimate (predict) the a priori client decision threshold for biometric recognition systems based on multiple linear regression. Biometric recognition is a complex classification problem where the goal is to classify a pattern (biometric sample) as belonging or not to a certain class (client). As in other pattern recognition problems, a correct estimation of the decision threshold is essential for optimizing the biometric system’s performance. Our proposal is tested in biometric signature recognition, estimating thresholds for different system working points. A theoretical and practical performance analysis is presented, including a comparison with the state of the art, showing the advantages, in system performance, of our proposal.
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Simon-Hurtado, A., Manso-Martínez, E., Vivaracho-Pascual, C., Pascual-Gaspar, J.M. (2012). A New Approach for a Priori Client Threshold Estimation in Biometric Signature Recognition Based on Multiple Linear Regression. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_22
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DOI: https://doi.org/10.1007/978-3-642-34487-9_22
Publisher Name: Springer, Berlin, Heidelberg
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