Applications of PCA and SVM-PSO Based Real-Time Face Recognition System
| Autor: | Ming-Yuan Shieh, Juing-Shian Chiou, Yu-Chia Hu, Kuo-Yang Wang |
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| Rok vydání: | 2014 |
| Předmět: |
Article Subject
Computer science General Mathematics ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Feature selection Machine learning computer.software_genre Facial recognition system Facial expression business.industry lcsh:Mathematics Dimensionality reduction General Engineering Swarm behaviour Pattern recognition lcsh:QA1-939 Support vector machine ComputingMethodologies_PATTERNRECOGNITION lcsh:TA1-2040 Computer Science::Computer Vision and Pattern Recognition Principal component analysis Combinatorial optimization Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business Classifier (UML) computer |
| Zdroj: | Mathematical Problems in Engineering, Vol 2014 (2014) |
| ISSN: | 1563-5147 1024-123X |
| DOI: | 10.1155/2014/530251 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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