Localization of multi-pose and occluded facial features via sparse shape representation

Yang Yu, Shaoting Zhang, Fei Yang, Dimitris Metaxas

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

3 Scopus citations

Abstract

Automatic facial feature localization plays an important role in many face identification and expression analysis algorithms. It is a challenging problem for real world images because of various face poses and occlusions. This paper proposes a unified framework to robustly locate multi-pose and occluded facial features. Instead of explicitly modeling the statistical point distribution, we use a sparse linear combination to approximate the observed shape, and hence alleviate the multi-pose problem. In addition, we use sparsity constraint to handle the outliers that can be caused by occlusions. We also model the initial misalignment and use convex optimization techniques to solve them simultaneously and efficiently. This proposed method has been extensively evaluated on both synthetic and real data, and the experimental results are promising.

Original languageEnglish (US)
Title of host publicationAdvances in Visual Computing - 9th International Symposium, ISVC 2013, Proceedings
Pages486-495
Number of pages10
EditionPART 1
DOIs
StatePublished - 2013
Event9th International Symposium on Advances in Visual Computing, ISVC 2013 - Rethymnon, Crete, Greece
Duration: Jul 29 2013Jul 31 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8033 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Symposium on Advances in Visual Computing, ISVC 2013
CountryGreece
CityRethymnon, Crete
Period7/29/137/31/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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