کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
534815 | 870294 | 2008 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel class-dependence feature analysis method for face recognition
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
This paper develops a novel Class-dependence Feature Analysis (CFA) method for robust face recognition. A new correlation filter called Optimal Origin Correlation output Tradeoff Filter (OOCTF) is designed in the two-dimensional (2-D) feature space obtained by Second-order Tensor Subspace Analysis (STSA). Designing correlation filters in the 2-D feature space makes them more tolerant to distortions in illumination and facial expression etc. Moreover, by focusing on the correlation outputs at the origin, OOCTF is very effective for feature vector extraction. Experimental results on three benchmark face databases show the superiority of the proposed method over traditional face recognition methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 29, Issue 14, 15 October 2008, Pages 1907–1914
Journal: Pattern Recognition Letters - Volume 29, Issue 14, 15 October 2008, Pages 1907–1914
نویسندگان
Yan Yan, Yu-Jin Zhang,