Face Recognition by Searching Most Similar Sample with Immune Learning
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.
KeywordsFace recognition most similar sample searching immune learning clonal selection immune memory
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- 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
- 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
- 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
- 23.Burnet, F.M.: The Clonal Selection Theory of Acquired Immunity. Cambridge University Press, Cambridge (1959)Google Scholar
- 26.Gong, T., Cai, Z.X.: Artificial Immune System Based on Normal Model and Its Applications. Tsinghua University Press, Beijing (2011)Google Scholar
- 27.Fu, K.S., Cai, Z.X., Xu, G.Y.: Artificial intelligence principles and applications. Tsinghua University Press, Beijing (1988)Google Scholar