کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
409442 | 679072 | 2006 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel dimensionality-reduction approach for face recognition
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
In this paper, we propose a novel dimensionality-reduction method—Fisher discriminant with Schur decomposition (FDS). Similar to Foley–Sammon discriminant analysis (FSD), FDS is an improvement of Fisher discriminant analysis (FDA) in that it eliminates linear dependences among discriminant vectors. In comparison with FSD, FDS is very simple in theory and realization. Experimental results conducted on two benchmark face-image databases, i.e. ORL and AR, demonstrate that FDS is highly effective and efficient in reducing dimensionalities of facial image spaces. Especially when the size of a database is large, FDS can even outperform the state-of-the-art facial feature extraction methods such as the null space method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 69, Issues 13–15, August 2006, Pages 1683–1687
Journal: Neurocomputing - Volume 69, Issues 13–15, August 2006, Pages 1683–1687
نویسندگان
Fengxi Song, David Zhang, Jingyu Yang,