Facial Feature Selection Based on SVMs by Regularized Risk Minimization

2006 ◽  
Author(s):  
Weihong Li ◽  
Weiguo Gong ◽  
Liping Yang ◽  
Weimin Chen ◽  
Xiaohua Gu
Keyword(s):  
Feature Selection ◽  
Facial Feature ◽  
Risk Minimization ◽  
Regularized Risk Minimization

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

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-Yuan Shieh ◽  
Juing-Shian Chiou ◽  
Yu-Chia Hu ◽  
Kuo-Yang Wang
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Real Time ◽  
Selection Process ◽  
Principal Component ◽  
Facial Feature ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Classification Problems ◽  
Recognition Systems

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.

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