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Age Classification Combining Contour and Texture Feature

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Neural Information Processing. Models and Applications (ICONIP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6444))

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Abstract

Age classification based on computer vision has widespread applications. Most of previous works only utilize texture feature or use contour and texture feature separately. In this paper, we proposed an age classification system that integrate contour and texture information. Besides, we improve the traditional Local Binary Pattern(LBP) feature extraction method and get pure texture feature. Support Vector Machines with probabilistic output (SVM-PO) is used as individual classifiers. Then we use combination mechanism based on fuzzy integral to merge the output of different classifiers. The experiment results show pure texture feature outperforms other features and it can be well combined with contour feature.

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Author information

Authors and Affiliations

  1. Center for Brain-Like Computing and Machine Intelligence,Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai, 200240, China

    Yan-Ming Tang & Bao-Liang Lu

  2. MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai, 200240, China

    Bao-Liang Lu

Authors
  1. Yan-Ming Tang
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  2. Bao-Liang Lu
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Editor information

Editors and Affiliations

  1. School of Information Technology, Murdoch University, 6150, Murdoch, WA, Australia

    Kok Wai Wong

  2. The Australian National University, 0200, Canberra, ACT, Australia

    B. Sumudu U. Mendis

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Avenue, 2522, P.O. Box, Wollongong, NSW, Australia

    Abdesselam Bouzerdoum

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© 2010 Springer-Verlag Berlin Heidelberg

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Tang, YM., Lu, BL. (2010). Age Classification Combining Contour and Texture Feature. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_61

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  • DOI: https://doi.org/10.1007/978-3-642-17534-3_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17533-6

  • Online ISBN: 978-3-642-17534-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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