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)


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, Y.N., Han, C.C., Wang, C.T., et al.: Face Recognition Using Nearest Feature Space Embedding. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(6), 1073–1086 (2011), doi:10.1109/TPAMI.2010.197CrossRefGoogle Scholar
  2. 2.
    He, X., Yan, S., Ho, Y., et al.: Face Recognition Using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005), doi:10.1109/TPAMI.2005.55CrossRefGoogle Scholar
  3. 3.
    Cai, D., He, X., Han, J., Zhang, H.: Orthogonal Laplacianfaces for Face Recognition. IEEE Trans. Image Processing 15(11), 3608–3614 (2006), doi:10.1109/TIP.2006.881945CrossRefGoogle Scholar
  4. 4.
    Tenenbaum, J., Silva, V., Langford, J.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290(22), 2319–2323 (2000)CrossRefGoogle Scholar
  5. 5.
    Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(22), 2323–2326 (2000)CrossRefGoogle Scholar
  6. 6.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997), doi:10.1109/34.598228Google Scholar
  7. 7.
    Lotlikar, R., Kothari, R.: Fractional-Step Dimensionality Reduction. IEEE Trans. Pattern Analysis and Machine Intelligence 22(6), 623–627 (2000), doi:10.1109/34.862200CrossRefGoogle Scholar
  8. 8.
    Yang, J., Zhang, D., Frangi, A., et al.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004), doi:10.1109/TPAMI.2004.10004CrossRefGoogle Scholar
  9. 9.
    Etemad, K., Chellappa, R.: Discriminant Analysis for Recognition of Human Face Recognition. J. Optical Soc. Am. 14(8), 1724–1733 (1997)CrossRefGoogle Scholar
  10. 10.
    Fortuna, J., Capson, D.: Improved Support Vector Classification Using PCA and ICA Feature Space Modification. Pattern Recognition 37(6), 1117–1129 (2004)CrossRefzbMATHGoogle Scholar
  11. 11.
    Swets, D., Weng, J.: Using Discriminant Eigenfeatures for Image Retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 18(8), 831–836 (1996), doi:10.1109/34.531802CrossRefGoogle Scholar
  12. 12.
    Rajagopalan, A.N., Chellappa, R., Koterba, N.: Background Learning for Robust Face Recognition with PCA in the Presence of Clutter. IEEE Trans. Image Processing 14(6), 832–843 (2005), doi:10.1109/TIP.2005.847288CrossRefGoogle Scholar
  13. 13.
    Janeway, C.A., Medzhitov, R.: Innate Immune Recognition. Annu. Rev. Immunol. 20, 197–216 (2002)CrossRefGoogle Scholar
  14. 14.
    Kofoed, E.M., Vance, R.E.: Innate immune recognition of bacterial ligands by NAIPs determines inflammasome specificity. Nature 10394, 1–6 (2011)Google Scholar
  15. 15.
    Iwasaki, A., Medzhitov, R.: Regulation of Adaptive Immunity by the Innate Immune System. Science 327(5963), 291–295 (2010)CrossRefGoogle Scholar
  16. 16.
    Huang, S.F., Wang, X., Yan, Q.Y., et al.: The Evolution and Regulation of the Mucosal Immune Complexity in the Basal Chordate Amphioxus. The Journal of Immunology 186(4), 2042–2055 (2011)CrossRefGoogle Scholar
  17. 17.
    Yirmiya, R., Goshen, I.: Immune modulation of learning, memory, neural plasticity and neurogenesis. Brain, Behavior, and Immunity 25(2), 181–213 (2011)CrossRefGoogle Scholar
  18. 18.
    Luh, G.C., Lin, C.Y.: PCA based immune networks for human face recognition. Applied Soft Computing 11(2), 1743–1752 (2011)CrossRefGoogle Scholar
  19. 19.
    Ma, W.P., Shang, R.H., Jiao, L.C.: A Novel Clonal Selection Algorithm for Face Detection. In: Sattar, A., Kang, B.-H. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 799–807. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Watkins, A., Timmis, J., Boggess, L.: Artificial Immune Recognition System (AIRS): An Immune Inspired Supervised Machine Learning Algorithm. Genetic Programming and Evolvable Machines 5(3), 291–317 (2004)CrossRefGoogle Scholar
  21. 21.
    Yu, S., Dasgupta, D.: Conserved Self Pattern Recognition Algorithm with Novel Detection Strategy Applied to Breast Cancer Diagnosis. Journal of Artificial Evolution and Applications, Special Issue on Artificial Evolution Methods in the Biological and Biomedical Sciences, 1–12 (January 2009)Google Scholar
  22. 22.
    de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)CrossRefGoogle Scholar
  23. 23.
    Burnet, F.M.: The Clonal Selection Theory of Acquired Immunity. Cambridge University Press, Cambridge (1959)Google Scholar
  24. 24.
    Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, pp. 138–142 (December 1994)CrossRefGoogle Scholar
  25. 25.
    Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression Database. IEEE Trans. Pattern Analysis and Machine Intelligence 25(12), 1615–1618 (2003)CrossRefGoogle Scholar
  26. 26.
    Gong, T., Cai, Z.X.: Artificial Immune System Based on Normal Model and Its Applications. Tsinghua University Press, Beijing (2011)Google Scholar
  27. 27.
    Fu, K.S., Cai, Z.X., Xu, G.Y.: Artificial intelligence principles and applications. Tsinghua University Press, Beijing (1988)Google Scholar

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

Personalised recommendations