این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Artificial Intelligence and Data Mining، جلد ۷، شماره ۱، صفحات ۱۷-۲۵
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Improving LNMF Performance of Facial Expression Recognition via Significant Parts Extraction using Shapley Value
چکیده انگلیسی مقاله Nonnegative Matrix Factorization (NMF) algorithms have been utilized in a wide range of real applications. NMF is done by several researchers to its part based representation property especially in the facial expression recognition problem. It decomposes a face image into its essential parts (e.g. nose, lips, etc.) but in all previous attempts, it is neglected that all features achieved by NMF do not need for recognition problem. For example, some facial parts do not have any useful information regarding the facial expression recognition. Addressing this challenge of defining and calculating the contributions of each part, the Shapley value is used. It is applied for identifying the contribution of each feature in the classification problem; then, affects less features are removed. Experiments on the JAFFE dataset and MUG Facial Expression Database as benchmarks of facial expression datasets demonstrate the effectiveness of our approach.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله M. Rezaei |
Computer Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran.

V. Derhami |
Faculty of Electrical & Computer Engineering, University of Sistan & Baluchestan, Zahedan, Iran.

نشانی اینترنتی http://jad.shahroodut.ac.ir/article_1109_7e2203e7a5ca2f2db958f1efa4f2b263.pdf
فایل مقاله اشکال در دسترسی به فایل - ./files/site1/rds_journals/480/article-480-1136282.pdf
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات