Adaptive biometric verification system using quality-based co-training

Tarek M. Mostafa, Iman A. El-Azab, Neamat F. El-Gayar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The performance of a biometric verification system may degrade substantially if the input samples vary significantly compared to existing samples in the gallery. Adaptive biometric systems that can improve with use, have recently gained popularity together with using semi-supervised learning methods for accommodating the continuous change in the subject's data. In this study we investigate the value of using quality measures of biometrics to incorporate it in a semi-supervised learning context for the design of an adaptive biometric system. The novelty of the proposed approach is the use of quality information of input samples as an extra source of information to update the user gallery. Our results show that using quality measures in the fusion process will improve system performance.

Original languageEnglish
Title of host publication2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)
PublisherIEEE
Pages1313-1318
Number of pages6
ISBN (Electronic)9781467303828
ISBN (Print)9781467303811
DOIs
Publication statusPublished - 24 Sep 2012
Event11th International Conference on Information Science, Signal Processing and their Applications 2012 - Montreal, QC, Canada
Duration: 2 Jul 20125 Jul 2012

Conference

Conference11th International Conference on Information Science, Signal Processing and their Applications 2012
Abbreviated titleISSPA 2012
CountryCanada
CityMontreal, QC
Period2/07/125/07/12

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing

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