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Intelligent Face Recognition based on Manifold Learning and Genetic-Chaos Algorithm Optimized Kernel Extreme Learning Machine

Wei He1, Enjun Wang2, and Ting Xiong3
1.Transportation Engineering Institute of Minjiang University, Fujian 350108, China
2.Transportation Research Center, Wuhan Institute of Technology, Wuhan 430073, China
3.School of Power and Energy Engineering, Wuhan University of Technology, Wuhan 430063, China
Abstract—In order to extract sensitive features of face images from high dimensional image data and facilitate the recognition speed, this paper has proposed a novel method based on the manifold learning and genetic-chaos algorithm optimized kernel extreme learning machine (KELM) for the application of face recognition. The locally linear embedding (LLE) algorithm has been employed to extract distinct features by projecting the original high dimensionality of the face image into a low dimensionality space. Then the KELM is introduced to provide quick and accurate pattern recognition on the extracted features. The only parameter need to be determined in KELM is the neuron number of hidden layer. Literature review indicates that very limited work has addressed the optimization of this parameter. Hence, the genetic-chaos algorithm was used for the first time to optimize the KELM parameter in this paper. A robust KELM structure may be attained after the genetic-chaos optimization. In order to evaluate and verify the proposed method, experiment tests have been carried out using standard face expressions. The experimental analysis results indicate that the performance of the proposed LLE- genetic-chaos-KELM method outperforms its rivals in terms of both recognition accuracy and training speed.

Index Terms—face recognition, feature extraction, GA Chaos, KELM.

Cite: Wei He, Enjun Wang, and Ting Xiong, "Intelligent Face Recognition based on Manifold Learning and Genetic-Chaos Algorithm Optimized Kernel Extreme Learning Machine," Journal of Communications, vol. 8, no. 10, pp. 658-664, 2013. doi: 10.12720/jcm.8.10.658-664
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