Citation

Chong, Siew Chin and Teoh, Andrew Beng Jin and Ong, Thian Song (2016) Weighted Discriminant Analysis and Kernel Ridge Regression Metric Learning for Face Verification. International Conference on Neural Information Processing, 9948. pp. 401-410. ISSN 0302-9743

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Abstract

A new formulation of metric learning is introduced by assimilating the kernel ridge regression (KRR) and weighted side-information linear discriminant analysis (WSILD) to enjoy the best of both worlds for unconstrained face verification task. To be specific, we formulate a doublet constrained metric learning problem by means of a second degree polynomial kernel function. The said metric learning problem can be solved analytically for Mahalanobis distance metric due to simplistic nature of KRR in which we named KRRML. In addition, the WSILD further enhances the learned Mahalanobis distance metric by leveraging the within-class and between-class scatter matrix of doublets. We evaluate the proposed method with Labeled Faces in the Wild database, a large benchmark dataset targeted for unconstrained face verification. The promising result attests the robustness and feasibility of the proposed method.

Item Type:	Article
Uncontrolled Keywords:	Kernel ridge regression, Metric learning, Face verification, Unconstrained
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	10 Jul 2020 09:16
Last Modified:	10 Jul 2020 09:16
URII:	http://shdl.mmu.edu.my/id/eprint/6767

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