Improving LNMF Performance of Facial Expression Recognition via Significant Parts Extraction using Shapley Value | ||
| Journal of AI and Data Mining | ||
| مقاله 2، دوره 7، شماره 1، فروردین 2019، صفحه 17-25 اصل مقاله (1.32 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2017.4676.1554 | ||
| نویسندگان | ||
| M. Rezaei1؛ V. Derhami* 2 | ||
| 1Computer Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran. | ||
| 2Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Non-negative Matrix Factorization (NMF)؛ Shapley value؛ Game Theory | ||
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