Titlebook: Adaptive Biometric Systems; Recent Advances and Ajita Rattani,Fabio Roli,Eric Granger Book 2015 Springer International Publishing Switzerl

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978-3-319-37222-8Springer International Publishing Switzerland 2015

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Ronald J. Gibbons,Johannes van Houte during testing and a restricted number of enrollment samples for training. Furthermore, biometric traits can change over time due to aging and change of lifestyle. Effectively, the noise factors encountered in testing cannot be represented by the limited training samples. A promising solution to tr

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Bacterial Cell Walls and Surfacesnd test samples. For co-training-based multimodal biometric systems, classical semi-supervised learning strategies such as self-training and co-training may not have fully exploited the advantage of a multimodal fusion, notably due to the fusion module. For this reason, this chapter discusses a

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https://doi.org/10.1007/978-1-4615-6514-7iometric samples are fused for taking decision on similarity. Proposed score normalization is an threshold alignment and range compression scheme. It utilizes statistical properties of the score distribution. The proposed scheme has been tested over a multimodal database which is constructed using t