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Latent identity variables: Biometric matching without explicit identity estimation

Prince, SJD; Aghajanian, J; Mohammed, U; Sahani, M; (2007) Latent identity variables: Biometric matching without explicit identity estimation. In: Lee, SW and Li, SZ, (eds.) Advances in Biometrics, Proceedings. (pp. 424 - 434). SPRINGER-VERLAG BERLIN

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Abstract

We present a new approach to biometrics that makes probabilistic inferences about matching without ever estimating an identity "template". The biometric data is considered to have been created by a noisy generative process. This process consists of (i) a deterministic component, which depends entirely on an underlying representation of identity and (ii) a stochastic component which accounts for the fact that two biometric samples from the same person are not identical. In recognition, we make inferences about whether the underlying identity representation is the same without ever estimating it. Instead we treat identity as fundamentally uncertain and consider all possible values in our decision. We demonstrate these ideas with toy examples from face recognition. We compare our approach to the class-conditional viewpoint.

Type:Proceedings paper
Title:Latent identity variables: Biometric matching without explicit identity estimation
Event:International Conference on Biometrics
Location:Seoul, SOUTH KOREA
Dates:2007-08-27 - 2007-08-29
ISBN-13:978-3-540-74548-8
Keywords:biometrics, face recognition, Bayesian methods, RECOGNITION
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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