Recognizing occluded faces by exploiting psychophysically inspired similarity maps

Ibrahim Venkat, Ahamad Tajudin Khader, K G Subramanian, Philippe De Wilde

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

The presence of occlusions in facial images is inevitable in unconstrained scenarios. However recognizing occluded faces remains a partially solved problem in computer vision. In this contribution we propose a novel Bayesian technique inspired by psychophysical mechanisms relevant to face recognition to address the facial occlusion problem. For some individuals certain facial regions, e.g. features comprising of some of the upper face, might be more discriminative than the rest of the features in the face. For others, it might be the features over the mid face and some of the lower face that are important. The proposed approach in this paper, will allow for such a psychophysical analysis to be factored into the recognition process. We have discovered and modeled similarity mappings that exist in facial domains by means of Bayesian Networks. The model can efficiently learn and exploit these mappings from the facial domain and hence capable of tackling uncertainties caused by occlusions. The proposed technique shows improved recognition rates over state of the art techniques. (C) 2012 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)903-911
Number of pages9
JournalPattern Recognition Letters
Volume34
Issue number8
DOIs
Publication statusPublished - 1 Jun 2013

Keywords

  • Face recognition
  • Occlusion
  • Bayesian Network
  • Machine learning
  • Similarity measures
  • EXTERNAL FEATURES
  • UNFAMILIAR FACES
  • RECOGNITION
  • MODEL
  • IDENTIFICATION
  • REPRESENTATION
  • ROBUST
  • ALGORITHMS
  • FAMILIAR
  • SALIENCY

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