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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 530251, 12 pages
Research Article

Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan City 710, Taiwan

Received 25 February 2014; Accepted 23 April 2014; Published 13 May 2014

Academic Editor: Rui Mu

Copyright © 2014 Ming-Yuan Shieh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper incorporates principal component analysis (PCA) with support vector machine-particle swarm optimization (SVM-PSO) for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.