淡江大學機構典藏:Item 987654321/106262
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 62819/95882 (66%)
造访人次 : 3997611      在线人数 : 561
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/106262


    题名: Effective semantic features for facial expressions recognition using SVM
    作者: Hsieh, Chen-Chiung;Hsih, Mei-Hua;Jiang, Meng-Kai;Cheng, Yun-Maw, Liang, En-Hui
    关键词: Face detection;Active shape model(ASM);Facial expression recognition;Facial texture;Support vector machine(SVM)
    日期: 2015-04-17
    上传时间: 2016-04-22 13:43:42 (UTC+8)
    出版者: Springer US
    摘要: Most traditional facial expression-recognition systems track facial components such as eyes, eyebrows, and mouth for feature extraction. Though some of these features can provide clues for expression recognition, other finer changes of the facial muscles can also be deployed for classifying various facial expressions. This study locates facial components by active shape model to extract seven dynamic face regions (frown, nose wrinkle, two nasolabial folds, two eyebrows, and mouth). Proposed semantic facial features could then be acquired using directional gradient operators like Gabor filters and Laplacian of Gaussian. A multi-class support vector machine (SVM) was trained to classify six facial expressions (neutral, happiness, surprise, anger, disgust, and fear). The popular Cohn–Kanade database was tested and the average recognition rate reached 94.7 %. Also, 20 persons were invited for on-line test and the recognition rate was about 93 % in a real-world environment. It demonstrated that the proposed semantic facial features could effectively represent changes between facial expressions. The time complexity could be lower than the other SVM based approaches due to the less number of deployed features.
    關聯: Multimedia Tools and Applications, pp.1-20
    DOI: 10.1007/s11042-015-2598-1
    显示于类别:[資訊管理學系暨研究所] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    Effective semantic features for facial expressions recognition using SVM.pdf1556KbAdobe PDF0检视/开启
    index.html0KbHTML189检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈