کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11031537 | 1645970 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Discriminative kernel-based metric learning for face verification
ترجمه فارسی عنوان
یادگیری متریک مبتنی بر هسته تشخیصی برای تایید چهره
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
تأیید صحت، یادگیری متریک، دستگاه هسته، تجزیه و تحلیل دائمی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
This paper outlines a simplistic formulation for doublet constrained discriminative metric learning framework for face verification. The Mahalanobis distance metric of the framework is formulated by leveraging the within-class scatter matrix of the doublet and a quadratic kernel function. Unlike existing metric learning methods, the proposed framework admits efficient solution attributed to the convexity nature of the kernel machines. We demonstrate three realizations of the proposed framework based on the well-known kernel machine instances, namely Support Vector Machine, Kernel Ridge Regression and Least Squares Support Vector Machine. Due to wide availability of off-the-shelf kernel learner solvers, the proposed method can be easily trained and deployed. We evaluate the proposed discriminative kernel-based metric learning with two types of face verification setup: standard and unconstrained face verification through three benchmark datasets. The promising experimental results corroborate the feasibility and robustness of the proposed framework.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 56, October 2018, Pages 207-219
Journal: Journal of Visual Communication and Image Representation - Volume 56, October 2018, Pages 207-219
نویسندگان
Siew-Chin Chong, Thian-Song Ong, Andrew Beng Jin Teoh,