article

A novel statistical generative model dedicated to face recognition

Authors:

Guillaume Heusch,

Sébastien MarcelAuthors Info & Claims

Image and Vision Computing, Volume 28, Issue 1

Pages 101 - 110

https://doi.org/10.1016/j.imavis.2009.05.001

Published: 01 January 2010 Publication History

Abstract

In this paper, a novel statistical generative model to describe a face is presented, and is applied to the face authentication task. Classical generative models used so far in face recognition, such as Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) for instance, are making strong assumptions on the observations derived from a face image. Indeed, such models usually assume that local observations are independent, which is obviously not the case in a face. The presented model hence proposes to encode relationships between salient facial features by using a static Bayesian Network. Since robustness against imprecisely located faces is of great concern in a real-world scenario, authentication results are presented using automatically localised faces. Experiments conducted on the XM2VTS and the BANCA databases showed that the proposed approach is suitable for this task, since it reaches state-of-the-art results. We compare our model to baseline appearance-based systems (Eigenfaces and Fisherfaces) but also to classical generative models, namely GMM, HMM and pseudo-2DHMM.

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Cited By

de Assis Angeloni MPedrini H(2016)Part-based representation and classification for face recognition2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2016.7844680(002900-002905)Online publication date: 9-Oct-2016
https://dl.acm.org/doi/10.1109/SMC.2016.7844680
Fan WSallay HBouguila NBourouis S(2015)A hierarchical Dirichlet process mixture of generalized Dirichlet distributions for feature selectionComputers and Electrical Engineering10.1016/j.compeleceng.2015.03.01843:C(48-65)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1016/j.compeleceng.2015.03.018
Hayashi EMaas MHong JJones MPalanque PSchmidt AGrossman T(2014)Wave to meProceedings of the SIGCHI Conference on Human Factors in Computing Systems10.1145/2556288.2557043(3453-3462)Online publication date: 26-Apr-2014
https://dl.acm.org/doi/10.1145/2556288.2557043
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A novel statistical generative model dedicated to face recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

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cover image Image and Vision Computing

Image and Vision Computing Volume 28, Issue 1

January, 2010

208 pages

ISSN:0262-8856

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Copyright © Elsevier B.V. © 2009.

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Butterworth-Heinemann

United States

Publication History

Published: 01 January 2010

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Cited By

de Assis Angeloni MPedrini H(2016)Part-based representation and classification for face recognition2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2016.7844680(002900-002905)Online publication date: 9-Oct-2016
https://dl.acm.org/doi/10.1109/SMC.2016.7844680
Fan WSallay HBouguila NBourouis S(2015)A hierarchical Dirichlet process mixture of generalized Dirichlet distributions for feature selectionComputers and Electrical Engineering10.1016/j.compeleceng.2015.03.01843:C(48-65)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1016/j.compeleceng.2015.03.018
Hayashi EMaas MHong JJones MPalanque PSchmidt AGrossman T(2014)Wave to meProceedings of the SIGCHI Conference on Human Factors in Computing Systems10.1145/2556288.2557043(3453-3462)Online publication date: 26-Apr-2014
https://dl.acm.org/doi/10.1145/2556288.2557043
Marqués IGraña M(2011)Experiments on lattice independent component analysis for face recognitionProceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II10.5555/2009542.2009573(286-294)Online publication date: 30-May-2011
https://dl.acm.org/doi/10.5555/2009542.2009573

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