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Subspaces Versus Submanifolds — A Comparative Study of Face Recognition

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Pattern Recognition, Machine Intelligence and Biometrics

Abstract

Automatic face recognition is a challenging problem in the biometrics area, where the dimension of the sample space is typically larger than the number of samples in the training set and consequently the so-called small sample size problem exists. Recently, neuroscientists emphasized the manifold ways of perception, and showed the face images may reside on a nonlinear submanifold hidden in the image space. Many manifold learning methods, such as Isometric feature mapping, Locally Linear Embedding, and Locally Linear Coordination are proposed. These methods achieved the submanifold by collectively analyzing the overlapped local neighborhoods and all claimed to be superior to such subspace methods as Eigenfaces and Fisherfaces in terms of classification accuracy. However, in literature, no systematic comparative study for face recognition is performed among them. In this paper, we carry out a comparative study among them in face recognition, and this study considers theoretical aspects as well as simulations performed using CMU PIE and FERET face databases.

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Authors and Affiliations

  1. Key Lab. on Opto-electronic Technique and Systems, Ministry of Education, Chongqing University, 400044, Chongqing, China

    Hong Huang

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  1. Hong Huang
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  1. CCIS, Northeastern University, 360 Huntington Ave, Boston, MA, 02110, USA

    Patrick S. P. Wang

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© 2011 Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg

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Huang, H. (2011). Subspaces Versus Submanifolds — A Comparative Study of Face Recognition. In: Wang, P.S.P. (eds) Pattern Recognition, Machine Intelligence and Biometrics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22407-2_18

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  • DOI: https://doi.org/10.1007/978-3-642-22407-2_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22406-5

  • Online ISBN: 978-3-642-22407-2

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