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International Conference on Artificial Immune Systems

ICARIS 2012: Artificial Immune Systems pp 1–13Cite as

Face Recognition by Searching Most Similar Sample with Immune Learning

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7597))

Abstract

Face recognition algorithms often have to filter out the disturbances of some conditional factors such as facial pose, illumination, and expression (PIE). So an increasing number of researchers have been figuring out the best discrimi-nant transformation in the feature space of faces to improve the recognition performance. They have also proposed novel feature-matching algorithms to minimize the PIE effects. For example, Chen et al. designed a nearest feature space (NFS) embedding algorithm that outperformed the other algorithms for face recognition. By searching the most similar sample with immune learning, in this paper, a novel algorithm is proposed to filter out the disturbances of PIE for face recognition. The adaptive adjustment for filtering out the disturbance of PIE is designed with immune memory to maximize the success possibility for recognizing the faces. The clonal selection frame is used to search the most similar samples to the target face, and the selected antibodies are memorized as the candidates for the best solution or the second optimal solution. The proposed approach is evaluated on several benchmark databases and is compared with the NFS embedding algorithm. The experimental results show that the proposed approach outperforms the NFS embedding algorithm.

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Author information

Authors and Affiliations

  1. College of Information S. & T., Donghua University, Shanghai, 201620, China

    Tao Gong

  2. Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University, Shanghai, 201620, China

    Tao Gong

  3. Department of Computer Science, Purdue University, West Lafayette, 47907, USA

    Tao Gong

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  1. Tao Gong
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Editors and Affiliations

  1. Departmento de Computación, Centro de Investigacion y de Estudios, Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Av. Insituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, 07300, Mexico, D.F., Mexico

    Carlos A. Coello Coello

  2. School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, NG8 1BB, Nottingham, UK

    Julie Greensmith

  3. School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, NG81BB, Nottingham, UK

    Natalio Krasnogor

  4. Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, CB3 0FD, Cambridge, UK

    Pietro Liò

  5. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Giuseppe Nicosia  & Mario Pavone  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Gong, T. (2012). Face Recognition by Searching Most Similar Sample with Immune Learning. In: Coello Coello, C.A., Greensmith, J., Krasnogor, N., Liò, P., Nicosia, G., Pavone, M. (eds) Artificial Immune Systems. ICARIS 2012. Lecture Notes in Computer Science, vol 7597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33757-4_1

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  • DOI: https://doi.org/10.1007/978-3-642-33757-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33756-7

  • Online ISBN: 978-3-642-33757-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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