期刊名称：The International Arab Journal of Information Technology
印刷版ISSN：1683-3198
出版年度：2012
卷号：9
期号：4
出版社：Zarqa Private University
摘要：Automatic facial expression recognition is a challenging problem in computer vision, and has gained significant importance in the applications of human-computer interactions. The vital component of any successful expression recognition system is an effective facial representation from face images. In this paper, we have derived an appearance-based feature descriptor, the Local Directional Pattern Variance (LDPv), which characterizes both the texture and contrast information of facial components. The LDPv descriptor is a collection of LDP codes weighted by their corresponding variances. The feature dimension is then reduced by extracting the most discriminative elements of the representation with Principal Component Analysis (PCA). The recognition performance based on our LDPv descriptor has been evaluated using Cohn-Kanade expression database with a Support Vector Machine (SVM) classifier. The discriminative strength of LDPv representation is also assessed over a useful range of low resolution images. Experimental results with prototypic expressions show that the LDPv descriptor has achieved a higher recognition rate, as compared to other existing appearance-based feature descriptors
关键词：Facial expression recognition; feature descriptor; Local Directional Pattern (LDP); LDP variance (LDPv); Principal Component Analysis (PCA); and Support Vector Machine (SVM) classifier
