Probabilistic Bayesian network classifier for face recognition in video sequences

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

2 Citations (Scopus)

Abstract

The inherent properties of video sequences allow for representation of data in both spatial and temporal dimensions. Using conventional image-based methods for face recognition in video is often an ineffective approach as the essential spatio-temporal properties are not fully harnessed. This paper proposes a probabilistic Bayesian network classifier to accomplish effective recognition of faces in video sequences. In our model, we introduce a joint probability function that encodes the causal dependencies between video frames, selected exemplars or representative images of a video, and subject classes. This enables both the temporal continuity between video frames and also the spatial relationships between exemplars and their respective exemplar-set classes to be captured. To simplify the tedious estimation of densities, the proposed method also utilizes probabilistic similarity scores that are computationally inexpensive. Good recognition rates were achieved by our proposed method in comprehensive experiments conducted on two standard face video datasets.

Original languageEnglish
Title of host publication11th International Conference on Intelligent Systems Design and Applications 2011
PublisherIEEE
Pages888-893
Number of pages6
ISBN (Electronic)9781457716768
DOIs
Publication statusPublished - 3 Jan 2012
Event11th International Conference on Intelligent Systems Design and Applications 2011 - Cordoba, Spain
Duration: 22 Nov 201124 Nov 2011

Publication series

NameInternational Conference on Intelligent Systems Design and Applications
ISSN (Print)2164-7143
ISSN (Electronic)2164-7151

Conference

Conference11th International Conference on Intelligent Systems Design and Applications 2011
Abbreviated titleISDA'11
CountrySpain
CityCordoba
Period22/11/1124/11/11

Keywords

  • Bayesian network
  • classifier
  • video-based face recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Control and Systems Engineering

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