Applications of PCA and SVM-PSO Based Real-Time Face Recognition System
Applications of PCA and SVM-PSO Based Real-Time Face Recognition System
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DOI：
10.1155/2014/530251
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发表时间：
2014-01-01
影响因子：
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通讯作者：
Wang, Kuo-Yang
中科院分区：
文献类型：
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作者：
Shieh, Ming-Yuan;Chiou, Juing-Shian;Wang, Kuo-Yang
本文结合主成分分析（PCA）和支持向量机-粒子群优化（SVM-PSO）开发实时人脸识别系统。该集成方案旨在采用SVM-PSO方法来提高基于PCA的图像识别系统对动态视觉感知的有效性。基于PCA的人脸识别方法具有降维的优点，因此在人机交互应用中，人脸识别大多采用PCA方法。然而，基于PCA的系统仅适用于处理具有相同面部表情和/或在相同视角方向下的面部。由于人脸特征选择过程可以被认为是机器学习中的全局组合优化问题，因此SVM-PSO通常被用作系统的最优分类器。在本文中，粒子群算法用于实现特征选择，支持向量机作为粒子群算法的适应度函数的分类问题。实验结果表明，该方法有效地简化了特征，并获得了较高的分类精度。
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.