کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
530271 869755 2015 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Locality Regularization Embedding for face verification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Locality Regularization Embedding for face verification
چکیده انگلیسی


• Regularized graph embedding approach for face verification.
• A regularization model adopts local Laplacian matrix to restore true data locality.
• Based on the proposed regularization model, three dimensionality reduction techniques are presented.

Graph embedding (GE) is a unified framework for dimensionality reduction techniques. GE attempts to maximally preserve data locality after embedding for face representation and classification. However, estimation of true data locality could be severely biased due to limited number of training samples, which trigger overfitting problem. In this paper, a graph embedding regularization technique is proposed to remedy this problem. The regularization model, dubbed as Locality Regularization Embedding (LRE), adopts local Laplacian matrix to restore true data locality. Based on LRE model, three dimensionality reduction techniques are proposed. Experimental results on five public benchmark face datasets such as CMU PIE, FERET, ORL, Yale and FRGC, along with Nemenyi Post-hoc statistical of significant test attest the promising performance of the proposed techniques.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 48, Issue 1, January 2015, Pages 86–102
نویسندگان
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