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A Confidence-Based Update Rule for Self-updating Human Face Recognition Systems

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 5558)


The aim of this paper is to present an automatic update rule to make a face recognition system adapt itself to the continuously changing appearance of users. The main idea is that every time the system interacts with a user, it adapts itself to include his or her current appearance, and thus, it always stays up-to-date. We propose a novel quality measure, which is used to decide whether the information just learnt from a user can be used to aggregate to what the system already knows. In the absence of databases that suit our needs, we present a publicly available database with 14,279 images of 35 users and 74 impostors acquired in a span of 5 months. Experiments on this database show that the proposed measure is adequate for a system to learn the current appearance of users in a non-supervised manner.


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© 2009 Springer-Verlag Berlin Heidelberg

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Pavani, SK., Sukno, F.M., Butakoff, C., Planes, X., Frangi, A.F. (2009). A Confidence-Based Update Rule for Self-updating Human Face Recognition Systems. In: Tistarelli, M., Nixon, M.S. (eds) Advances in Biometrics. ICB 2009. Lecture Notes in Computer Science, vol 5558. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01792-6

  • Online ISBN: 978-3-642-01793-3

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