Abstract
This paper describes an empirical study to investigate the performance of a wide range of classifiers deployed in applications to classify biometric data. The study specifically reports results based on two different modalities, the handwritten signature and fingerprint recognition. We demonstrate quantitatively how performance is related to classifier type, and also provide a finer-grained analysis to relate performance to specific non-biometric factors in population demographics. The paper discusses the implications for individual modalities, for multiclassifier but single modality systems, and for full multibiometric solutions.
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Abreu, M., Fairhurst, M. (2008). An Empirical Comparison of Individual Machine Learning Techniques in Signature and Fingerprint Classification. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds) Biometrics and Identity Management. BioID 2008. Lecture Notes in Computer Science, vol 5372. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89991-4_14
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DOI: https://doi.org/10.1007/978-3-540-89991-4_14
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