Improved immune computation for high-precision face recognition
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Dangerous persons can be monitored accurately and quickly using the high-precision recognition of the relative facial images via the image sensors. To increase the recognition rate of face recognition by improving the immune algorithm, the immune computation was redesigned for better face recognition to decrease the facial disturbances of the pose, illumination and expression (PIE), in this paper. The clonal selection algorithm was improved with the modification of the affinity and the algorithm workflow. The improved clonal selection algorithm searches the most similar antibody sample against the antigen of an unknown facial image, according to the affinity between the antigen and the antibody. The unknown facial images were recognized with this improved affinity and the uncertainty-based reasoning, so the affinity matching of the antibody with the unknown antigen was also improved. Experimental results show that this immune algorithm outperforms some state-of-the-art algorithms in the face recognition accuracy tests with such facial image databases as AR, Yale and CMU-PIE. So the proposed immune algorithm is useful and effective to improve the performance of face recognition in the image sensor network.
KeywordsFace recognition PIE High precision Clonal selection algorithm Improved affinity Unknown facial image Uncertainty-based reasoning
This study was funded by the Grants from National Natural Science Foundation of China (61673007, 61271114), Shanghai Natural Science Foundation (13ZR1400200) and the Fundamental Research Funds for the Central Universities at Donghua Univ. (2232013A3-09).
Compliance with ethical standards
Conflict of interest
Tao Gong declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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