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International Conference on Fuzzy Systems and Knowledge Discovery

FSKD 2005: Fuzzy Systems and Knowledge Discovery pp 1245–1254Cite as

Probabilistic Based Recursive Model for Face Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3614))

Abstract

We present a facial recognition system based on a probabilistic approach to adaptive processing of Human Face Tree Structures. Human Face Tree Structures are made up of holistic and localized Gabor Features. We propose extending the recursive neural network model by Frasconi et. al. [1] in which its learning algorithm was carried out by the conventional supervised back propagation learning through the tree structures, by making use of probabilistic estimates to acquire discrimination and obtain smooth discriminant boundaries at the structural pattern recognition. Our proposed learning framework of this probabilistic structured model is hybrid learning in locally unsupervised for parameters in mixture models and in globally supervised for weights in feed-forward models. The capabilities of the model in a facial recognition system are evaluated. The experimental results demonstrate that the proposed model significantly improved the recognition rate in terms of generalization.

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Author information

Authors and Affiliations

  1. Forensics and Security Lab, Division of Computing Systems, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore

    Siu-Yeung Cho & Jia-Jun Wong

Authors
  1. Siu-Yeung Cho
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  2. Jia-Jun Wong
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Cho, SY., Wong, JJ. (2005). Probabilistic Based Recursive Model for Face Recognition. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_164

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  • DOI: https://doi.org/10.1007/11540007_164

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28331-7

  • Online ISBN: 978-3-540-31828-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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