Psychophysically inspired Bayesian occlusion model to recognize occluded faces

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

*Corresponding author for this work

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

4 Citations (Scopus)


Face recognition systems robust to major occlusions have wide applications ranging from consumer products with biometric features to surveillance and law enforcement applications. In unconstrained scenarios, faces are often subject to occlusions, apart from common variations such as pose, illumination, scale, orientation and so on. In this paper we propose a novel Bayesian oriented occlusion model inspired by psychophysical mechanisms to recognize faces prone to occlusions amidst other common variations. We have discovered and modeled similarity maps that exist in facial domains by means of Bayesian Networks. The proposed model is capable of efficiently learning and exploiting these maps from the facial domain. Hence it can tackle the occlusion uncertainty reasonably well. Improved recognition rates over state of the art techniques have been observed.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns
Subtitle of host publication14th International Conference, CAIP 2011, Seville, Spain, August 29-31, 2011, Proceedings, Part I
EditorsPedro Real, Daniel Diaz-Pernil, Helena Molina-Abril, Ainhoa Berciano, Walter Kropatsch
Number of pages7
ISBN (Electronic)978-3-642-23672-3
ISBN (Print)978-3-642-23671-6
Publication statusPublished - 2011
Event14th International Conference on Computer Analysis of Images and Patterns - Seville, Spain
Duration: 29 Aug 201131 Aug 2011

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference14th International Conference on Computer Analysis of Images and Patterns
Abbreviated titleCAIP 2011


  • Face Recognition
  • Occlusion Models
  • Similarity Measures
  • Bayesian Networks
  • Parameter Estimation


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