Fusing cluster-centric feature similarities for face recognition in video sequences

John See, Mohammad Faizal Ahmad Fauzi, C. Eswaran

Research output: Contribution to journalArticlepeer-review

Abstract

The emergence of video has presented new challenges to the problem of face recognition. Most of the existing methods are focused towards the use of either representative exemplars or image sets to summarize videos. There is little work as to how they can be combined effectively to harness their individual strengths. In this paper, we investigate a new dual-feature approach to face recognition in video sequences that unifies feature similarities derived within local appearance-based clusters. Relevant similarity matching involving exemplar points and cluster subspaces are comprehensively modeled within a Bayesian maximum-a posteriori (MAP) classification framework. An extensive performance evaluation of the proposed method on three face video datasets have demonstrated promising results.

Original languageEnglish
Pages (from-to)2057-2064
Number of pages8
JournalPattern Recognition Letters
Volume34
Issue number16
DOIs
Publication statusPublished - 1 Dec 2013

Keywords

  • Bayesian MAP classification
  • Clusters
  • Exemplars
  • Feature fusion
  • Video-based face recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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