کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6938164 1449922 2018 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hierarchical uncorrelated multiview discriminant locality preserving projection for multiview facial expression recognition
ترجمه فارسی عنوان
طرح ریزی حفظ موقعیت مکانی چند منظوره سلسله مراتبی غیر متقابل برای تشخیص بیان چندگانه چهره
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Existing multi-view facial expression recognition algorithms are not fully capable of finding discriminative directions if the data exhibits multi-modal characteristics. This research moves toward addressing this issue in the context of multi-view facial expression recognition. For multi-modal data, local preserving projection (LPP) or local Fisher discriminant analysis (LFDA)-based approach is quite appropriate to find a discriminative space. Also, the classification performance can be enhanced by imposing uncorrelated constraint onto the discriminative space. So for multi-view (multi-modal) data, we proposed an uncorrelated multi-view discriminant locality preserving projection (UMvDLPP)-based approach to find an uncorrelated common discriminative space. Additionally, the proposed UMvDLPP is implemented in a hierarchical fashion (H-UMvDLPP) to obtain an optimal performance. Extensive experiments on BU3DFE dataset show that UMvDLPP performs slightly better than the existing methods. However, an improvement of approximately 3% as compared to the existing state-of-the-art multi-view learning-based approaches is achieved by our H-UMvDLPP. This improvement is due to the fact that the proposed method enhances the discrimination between the classes more effectively, and classifies expressions category-wise followed by classification of the basic expressions embedded in each of the subcategories (hierarchical approach).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 54, July 2018, Pages 171-181
نویسندگان
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