文章基本信息

标题：Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique
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作者：Jeemoni Kalita ; Karen Das
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2013
卷号：4
期号：2
DOI：10.14569/IJACSA.2013.040229
出版社：Science and Information Society (SAI)
摘要：In this paper, an Eigenvector based system has been presented to recognize facial expressions from digital facial images. In the approach, firstly the images were acquired and cropping of five significant portions from the image was performed to extract and store the Eigenvectors specific to the expressions. The Eigenvectors for the test images were also computed, and finally the input facial image was recognized when similarity was obtained by calculating the minimum Euclidean distance between the test image and the different expressions.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Facial expression recognition; facial expressions; Eigenvectors; Eigenvalues