کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
407285 678135 2016 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Graph Regularized Sparsity Discriminant Analysis for face recognition
ترجمه فارسی عنوان
تجزیه و تحلیل افتراقی انحراف معیار برای تشخیص چهره
کلمات کلیدی
نمایندگی انحصاری، تعبیه گراف، طرح ریزی حفظ انبساط، استخراج ویژگی، شناسایی چهره
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Manifold learning and Sparse Representation Classifier are two popular techniques for face recognition. Because manifold learning can find low-dimensional representations for high-dimensional data, it is widely applied in computer vision and pattern recognition. Most of the manifold learning algorithms can be unified in the graph embedding framework, where the first step is to determine the adjacent graphs. Traditional methods use kk nearest neighbor or the εε-ball schemes. However, they are parametric and sensitive to noises. Moreover, it is hard to determine the size of appropriate neighborhoods. To deal with these problems, in this paper, Graph Regularized Sparsity Discriminant Analysis, GRSDA, for short, is proposed. Based on graph embedding and sparsity preserving projection, the weight matrices for intrinsic and penalty graphs are obtained through sparse representation. GRSDA seeks a subspace in which samples in intra-classes are as compact as possible while samples in inter-classes are as separable as possible. Specifically, samples in the low-dimensional space can preserve the sparse locality relationship in the same class, while enhancing the separability for samples in different classes. Hence, GRSDA can achieve better performance. Extensive experiments were carried out on ORL, YALE-B and AR face databases, and the results confirmed that the proposed algorithm outperformed LPP, UDP, SPP and DSNPE.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 173, Part 2, 15 January 2016, Pages 290–297
نویسندگان
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