Abstract
This paper proposes a novel binary particle swarm optimization (PSO) algorithm using artificial immune system (AIS) for face recognition. Inspired by face recognition ability in human visual system (HVS), this algorithm fuses the information of the holistic and partial facial features. The holistic facial features are extracted by using principal component analysis (PCA), while the partial facial features are extracted by non-negative matrix factorization with sparseness constraints (NMFs). Linear discriminant analysis (LDA) is then applied to enhance adaptability to illumination and expression. The proposed algorithm is used to select the fusion rules by minimizing the Bayesian error cost. The fusion rules are finally applied for face recognition. Experimental results using UMIST and ORL face databases show that the proposed fusion algorithm outperforms individual algorithm based on PCA or NMFs.
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Notes
The ORL database is available from http://www.cam-orl.co.uk/facedatabase.html
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The authors would like to thank Miss Sheekha for proof reading this paper.
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This work was supported by National Science Foundation of China under Grant 60471055, and UESTC Youth Fund under Grant L08010601JX04030.
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Pu, X., Yi, Z. & Fang, Z. Holistic and partial facial features fusion by binary particle swarm optimization. Neural Comput & Applic 17, 481–488 (2008). https://doi.org/10.1007/s00521-007-0148-0
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DOI: https://doi.org/10.1007/s00521-007-0148-0