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Face Recognition by Searching Most Similar Sample with Immune Learning

  • Tao Gong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Face recognition most similar sample searching immune learning clonal selection immune memory 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tao Gong
    • 1
    • 2
    • 3
  1. 1.College of Information S. & T.Donghua UniversityShanghaiChina
  2. 2.Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of EducationDonghua UniversityShanghaiChina
  3. 3.Department of Computer SciencePurdue UniversityWest LafayetteUSA

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