Citation

Chong, Siew Chin and Ong, Thian Song and Teoh, Andrew Beng Jin (2018) Discriminative kernel-based metric learning for face verification. Journal of Visual Communication and Image Representation, 56. pp. 207-219. ISSN 1047-3203

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Kernel functions, Face verification, Metric learning, Kernel machine, Discriminant analysis
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	24 Nov 2020 12:35
Last Modified:	24 Nov 2020 12:35
URII:	http://shdl.mmu.edu.my/id/eprint/7442

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