Biometric authentication using mobile devices is becoming a convenient and important means to secure access to remote services such as telebanking and electronic transactions. Such an application poses a very challenging pattern recognition problem: the training samples are often sparse and they cannot represent the biometrics of a person. The query features are easily affected by the acquisition environment, the user’s accessories, occlusions and aging. Semi-supervised learning – learning from the query/test data – can be a means to tap the vast unlabeled training data. While there is evidence that semi-supervised learning can work in text categorization and biometrics, its application on mobile devices remains a great challenge. As a preliminary, yet, indispensable study towards the goal of semi-supervised learning, we analyze the following sub-problems: model adaptation, update criteria, inference with several models and user-specific time-dependent performance assessment, and explore possible solutions and research directions.

Challenges and Research Directions for Adaptive Biometric Recognition Systems

ROLI, FABIO
2009-01-01

Abstract

Biometric authentication using mobile devices is becoming a convenient and important means to secure access to remote services such as telebanking and electronic transactions. Such an application poses a very challenging pattern recognition problem: the training samples are often sparse and they cannot represent the biometrics of a person. The query features are easily affected by the acquisition environment, the user’s accessories, occlusions and aging. Semi-supervised learning – learning from the query/test data – can be a means to tap the vast unlabeled training data. While there is evidence that semi-supervised learning can work in text categorization and biometrics, its application on mobile devices remains a great challenge. As a preliminary, yet, indispensable study towards the goal of semi-supervised learning, we analyze the following sub-problems: model adaptation, update criteria, inference with several models and user-specific time-dependent performance assessment, and explore possible solutions and research directions.
978-3-642-01792-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/29001
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