Citation

Chong, Siew Chin and Chong, Lee Ying and Ong, Thian Song (2020) Discriminative Spectral Regression Metric Learning in Unconstrained Face Verification. In: 2020 8th International Conference on Information and Communication Technology (ICoICT), 24-26 June 2020, Yogyakarta, Indonesia.

Text 37.pdf - Published Version Restricted to Repository staff only Download (154kB)

Abstract

This paper presents the robustness of the proposed metric learning formulation, dubbed Discriminative Spectral Regression Metric Learning in offering a simplistic solution for measuring the Mahalanobis metric to solve unconstrained face verification problems. It takes advantage of distance metric learning on pairs of doublets by adopting the merit of the quadratic kernel function in the verification task. To be specific, the spectral graph analysis and the linear discriminant analysis are unified into the distance metric learning process for better exploitation of the intrinsic discriminant structure of face data. The proposed formulation is evaluated with four benchmarked constrained and unconstrained face datasets, with different tuning parameters under the restricted protocol. The promising result of 89.07% verification rate evinces the effectiveness and feasibility of the proposed formulation in unconstrained face verification compared to the state-of-the-art methods.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Face recognition
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	13 Oct 2021 03:10
Last Modified:	13 Oct 2021 03:10
URII:	http://shdl.mmu.edu.my/id/eprint/8284

Downloads

Edit (login required)