کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532802 869994 2008 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Fractional order singular value decomposition representation for face recognition
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Fractional order singular value decomposition representation for face recognition
چکیده انگلیسی

Face representation (FR) plays a typically important role in face recognition and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) have been received wide attention recently. However, despite of the achieved successes, these FR methods will inevitably lead to poor classification performance in case of great facial variations such as expression, lighting, occlusion and so on, due to the fact that the image gray value matrices on which they manipulate are very sensitive to these facial variations. In this paper, we take notice of the facts that every image matrix can always have the well-known singular value decomposition (SVD) and can be regarded as a composition of a set of base images generated by SVD, and we further point out that the leading base images (those corresponding to large singular values) on one hand are sensitive to the aforementioned facial variations and on the other hand dominate the composition of the face image. Then based on these observations, we subtly deflate the weights of the facial variation sensitive base images by a parameter αα and propose a novel fractional order singular value decomposition representation (FSVDR) to alleviate facial variations for face recognition. Finally, our experimental results show that FSVDR can: (1) effectively alleviate facial variations; and (2) form an intermediate representation for many FR methods such as PCA and LDA to significantly improve their classification performance in case of great facial variations.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 41, Issue 1, January 2008, Pages 378–395
نویسندگان
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