Consensus of regression for occlusion-robust facial feature localization

Xiang Yu, Zhe Lin, Jonathan Brandt, Dimitris N. Metaxas

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

27 Scopus citations

Abstract

We address the problem of robust facial feature localization in the presence of occlusions, which remains a lingering problem in facial analysis despite intensive long-term studies. Recently, regression-based approaches to localization have produced accurate results in many cases, yet are still subject to significant error when portions of the face are occluded. To overcome this weakness, we propose an occlusion-robust regression method by forming a consensus from estimates arising from a set of occlusion-specific regressors. That is, each regressor is trained to estimate facial feature locations under the precondition that a particular pre-defined region of the face is occluded. The predictions from each regressor are robustly merged using a Bayesian model that models each regressor's prediction correctness likelihood based on local appearance and consistency with other regressors with overlapping occlusion regions. After localization, the occlusion state for each landmark point is estimated using a Gaussian MRF semi-supervised learning method. Experiments on both non-occluded and occluded face databases demonstrate that our approach achieves consistently better results over state-of-the-art methods for facial landmark localization and occlusion detection.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2014 - 13th European Conference, Proceedings
PublisherSpringer Verlag
Pages105-118
Number of pages14
EditionPART 4
ISBN (Print)9783319105925
DOIs
StatePublished - 2014
Event13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland
Duration: Sep 6 2014Sep 12 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume8692 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th European Conference on Computer Vision, ECCV 2014
Country/TerritorySwitzerland
CityZurich
Period9/6/149/12/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Consensus of Regression
  • Face alignment
  • Facial feature localization
  • Occlusion detection

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