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Vol. 161, Issue 12, December 2013, pp. 34-38

 

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A Novel Method for Face Recognition based on Genetic Algorithm Optimized Kernel Extreme Learning Machine
 
Jiajun ZHANG

School of Mathematics and Statistics, Zaozhuang University, Zaozhuang, China
Tel.: +86-632-3786721, fax: +86-632-3786721
E-mail: zhangJiajun1975@yeah.net

 

Received: 20 September 2013   /Accepted: 22 November 2013   /Published: 30 December 2013

Digital Sensors and Sensor Sysstems

 

Abstract: The artificial intelligent classifiers have been proven to be efficient in face recognition; however, to meet the demand of online recognition, they need enhance the recognition accuracy and speed. In order to resolve this issue, the kernel extreme learning machine (KELM) has been proposed to provide quick and accurate pattern recognition ability. The only parameter need be determined in KELM is the neuron number of hidden layer. Suitable neuron number will accelerate the training procedure. However, little work has been done to select proper neuron number in the application of face recognition. To address this issue, this paper presents a new method that uses the genetic algorithm (GA) to optimize the KELM parameter for face recognition. After the determination of proper hidden layer neuron number, the face recognition accuracy and speed of KELM could meet the online application requirements. Experiments have been carried out to evaluate the proposed method. The performance of the GA-KELM was compared with KELM, ELM, and LS-SVM. The analysis results indicate that the proposed GA-KELM outperforms its rivals in terms of both recognition accuracy and training speed.

 

Keywords: Biometric identification, Face recognition, GA, KELM.

 

 

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