3D constrained local model with independent component analysis and non-Gaussian shape prior distribution: Application to 3D facial landmark detection

Marwa C.El Rai, Claudio Tortorici, Hassan Al-Muhairi, Marius George Linguraru, Naoufel Werghi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a novel statistical shape model and fitting process for the 3D Constrained Local Models (CLM), exploiting the properties of Independent Component Analysis (ICA), instead of the classic use of Principal Component Analysis (PCA), and adopting a non-Gaussian distribution of the shape prior information. Using ICA permits to exploit the real distribution of shape priors by adopting a Generalised Gaussian Distribution (GGD) model. Consequently, we derive a modified approach of the mean shift optimizer by using the Expectation-Maximization algorithms. We apply this novel method for the localization of face landmarks on 3D facial mesh models, which, to the best of our knowledge, is the first employment of the CLM variant on this kind of modality. Experiments conduced on the Bosphorus face database demonstrated that our approach outperforms state-of-the-art methods.

Original languageBritish English
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages3204-3208
Number of pages5
ISBN (Electronic)9781467399616
DOIs
StatePublished - 3 Aug 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: 25 Sep 201628 Sep 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Conference

Conference23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States
CityPhoenix
Period25/09/1628/09/16

Keywords

  • CLM
  • Facial Landmarks detection
  • Gaussian Generalized Distribution
  • ICA
  • Mesh-LBP

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