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Evolutionary-Rough Feature Selection for Face Recognition

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Transactions on Rough Sets XII

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 6190))

Abstract

Elastic Bunch Graph Matching is a feature-based face recognition algorithm which has been used to determine facial attributes from an image. However the dimension of the feature vectors, in case of EBGM, is quite high. Feature selection is a useful preprocessing step for reducing dimensionality, removing irrelevant data, improving learning accuracy and enhancing output comprehensibility.

In rough set theory reducts are the minimal subsets of attributes that are necessary and sufficient to represent a correct decision about classification. The high complexity of the problem has motivated investigators to apply various approximation techniques like the multi-objective GAs to find near optimal solutions for reducts.

We present here an application of the evolutionary-rough feature selection algorithm to the face recognition problem. The input corresponds to biometric features, modeled as Gabor jets at each node of the EBGM. Reducts correspond to feature subsets of reduced cardinality, for efficiently discriminating between the faces. The whole process is optimized using MOGA. The simulation is performed on large number of Caucasian and Indian faces, using the FERET and CDAC databases. The merit of clustering and their optimality is determined using cluster validity indices. Successful retrieval of faces is also performed.

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Author information

Authors and Affiliations

  1. CDAC, Kolkata, Salt Lake Electronics Complex, Plot - E2/1, Block- GP, Sector - V, Kolkata, 700091, India

    Debasis Mazumdar & Soma Mitra

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700 108, India

    Sushmita Mitra

Authors
  1. Debasis Mazumdar
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  2. Soma Mitra
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  3. Sushmita Mitra
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, R3T 5V6, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Insitute of Computing Science, Laboratory of Intelligent Decision Support Systems, Poznań University of Technology, Piotrowo 2, 60-965, Poznań, Poland

    Roman Słowiński

  4. Department of Mathematics and Computing Science, Saint Mary’s University, B3H 3C3, Halifax, NS, Canada

    Pawan Lingras

  5. School of Electronics and Information Engineering, Tongji University, 201804, Shanghai, P.R. China

    Duoqian Miao

  6. Faculty of Medicine, Department of Medical Informatics, 89-1 Enya-cho, Shimane University, 693-8501, Izumo, Japan

    Shusaku Tsumoto

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Mazumdar, D., Mitra, S., Mitra, S. (2010). Evolutionary-Rough Feature Selection for Face Recognition. In: Peters, J.F., Skowron, A., Słowiński, R., Lingras, P., Miao, D., Tsumoto, S. (eds) Transactions on Rough Sets XII. Lecture Notes in Computer Science, vol 6190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14467-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-14467-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14466-0

  • Online ISBN: 978-3-642-14467-7

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