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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/59911


    Title: Face Detection using SVM-Based Classification
    Authors: Yeh, Jih-Pin;Pai, I-Chun, Wang, Chun-Wei;Yang, Fu-Wen;Lin, Hwei-Jen
    Contributors: 淡江大學資訊工程學系
    Keywords: face detection;skin color segmentation;RGB color space;HSV color space;support vector machine;SVM
    Date: 2009-08
    Issue Date: 2011-10-05 22:26:08 (UTC+8)
    Publisher: Allahabad: Pushpa Publishing House
    Abstract: This paper proposes an improved version of our previously introduced face detection system based on skin color segmentation and neural networks. The new system, using a support vector machine (SVM) based method for learning and verification, consists of several stages. First, the system searches for the regions where faces might exist by using skin color information and forms a so-called skin map. After performing noise removal and some morphological operations on the skin map, it utilizes the aspect ratio of a face to find out possible face blocks, and then eye detection is carried out within each possible face block. If an eye pair is detected in a possible face block, a region is cropped according to the location of the two eyes, which is called a face candidate; otherwise, it is regarded as a non-face block. Finally, each of the face candidates is verified by a support vector machine. Experimental results reflect that the new version improves the verification accuracy of the previously proposed system.
    Relation: Far East Journal of Experimental and Theoretical Artificial Intelligence 3(2), pp.113-123
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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