Video-based face recognition using Exemplar-Driven Bayesian Network classifier

John See, Mohammad Faizal Ahmad Fauzi, Chikkannan Eswaran

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

3 Citations (Scopus)

Abstract

Many recent works in video-based face recognition involved the extraction of exemplars to summarize face appearances in video sequences. However, there has been a lack of attention towards modeling the causal relationship between classes and their associated exemplars. In this paper, we propose a novel Exemplar-Driven Bayesian Network (EDBN) classifier for face recognition in video. Our Bayesian framework addresses the drawbacks of typical exemplar-based approaches by incorporating temporal continuity between consecutive video frames while encoding the causal relationship between extracted exemplars and their parent classes within the framework. Under the EDBN framework, we describe a non-parametric approach of estimating probability densities using similarity scores that are computationally quick. Comprehensive experiments on two standard face video datasets demonstrated good recognition rates achieved by our method.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
PublisherIEEE
Pages372-377
Number of pages6
ISBN (Electronic)9781457702426
ISBN (Print)9781457702433
DOIs
Publication statusPublished - 2 Feb 2012
Event2nd IEEE International Conference on Signal and Image Processing Applications 2011 - Kuala Lumpur, Malaysia
Duration: 16 Nov 201118 Nov 2011

Conference

Conference2nd IEEE International Conference on Signal and Image Processing Applications 2011
Abbreviated titleICSIPA 2011
Country/TerritoryMalaysia
CityKuala Lumpur
Period16/11/1118/11/11

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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