Face verification based on bagging RBF networks

  • Yunhong Wang*
  • , Yiding Wang*
  • , Anil K. Jain
  • , Tieniu Tan
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Face verification is useful in a variety of applications. A face verification system is vulnerable not only to variations in ambient lighting, facial expression and facial pose, but also to the effect of small sample size during the training phase. In this paper, we propose an approach to face verification based on Radial Basis Function (RBF) networks and bagging. The technique seeks to offset the effect of using a small sample size during the training phase. The RBF networks are trained using all available positive samples of a subject and a few randomly selected negative samples. Bagging is then applied to the outputs of these RBF-based classifiers. Theoretical analysis and experimental results show the validity of the proposed approach.

Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2006, Proceedings
Pages69-77
Number of pages9
StatePublished - 2006
EventInternational Conference on Biometrics, ICB 2006 - Hong Kong, China
Duration: 5 Jan 20067 Jan 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3832 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Biometrics, ICB 2006
Country/TerritoryChina
CityHong Kong
Period5/01/067/01/06

Fingerprint

Dive into the research topics of 'Face verification based on bagging RBF networks'. Together they form a unique fingerprint.

Cite this