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Vol. 153, No. 6, June 2013, pp. 92-99




Combination of Local Multiple Patterns and Exponential Discriminant Analysis for Facial Recognition
1, 2 Lifang Zhou, 2 Bin Fang, 3 Weisheng Li, 3 Lidou Wang

1 College of Computer Science, Chongqing University, Chongqing, 400030, China

2 College of software, Chongqing University of Posts and Telecommunications Chongqing, 400065, China

3 Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

1 Tel.: 023-65112784, fax: 023-65112784 1 E-mail: zhoulf@cqupt.edu.cn


Received: 26 April 2013   /Accepted: 14 June 2013   /Published: 25 June 2013

Digital Sensors and Sensor Sysstems


Abstract: Global features-based methods and local features -based methods have been very successful in face recognition system, yet they can be combined together and jointly optimized so as to minimize the error of a nearest-neighbor classifier. We consider both descriptor for face images with Local Multiple Pattern, and discriminant learning techniques with Exponential Discriminant Analysis. A combination framework based on Local Multiple Pattern and Exponential Discriminant Analysis has been proposed in this paper. Firstly, our approach encodes the multi-scale face feature by Local Multiple Pattern, and then they have been extended to strengthen the discriminative ability by Exponential Discriminant Analysis; Secondly, we suggest to use the above feature on different layers independently so that a multiple classifier system can be attained. Using these techniques, we obtain the state-of-the-art performance on two public available databases.


Keywords: Face recognition, Local multiple pattern, Exponential discriminant analysis, Combination framework, Multiple classifier


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