Abstract
In Recent years, with the rapid development of facial expression recognition technology, processing and classification of facial expression recognition has become a hotspot in application studies of remote sensing. Rough set theory (RS) and SVM have unique advantages in information processing and classification. This paper applies RS-SVM to facial expression recognition, briefly introduce the concepts of RS and principle of SVM, attributes reduction in RS theory as preposing system to get rid of redundancy attributes. Meanwhile, the SVM classifier works as postposing system helps training and classifying the facial expression recognition. Experimental results indicate this model not only raise the operating speed, but also improve classification performance, providing a new effective way in facial expression recognition technology.
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