3D facial model synthesis using coupled dictionaries

Swami Sankaranarayanan, Vishal M. Patel, Rama Chellappa

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

Abstract

In this work, we propose a generative way of modeling faces, where the 3D shape of a face is generated by a supervised learning procedure involving coupled sparse feature learning. To learn dictionaries using the proposed method, we use the USF-HUMAN ID database [1]. We provide as input to our training system, paired correspondences of 2D and 3D images of individuals and aim to learn the low-level patches both in 2D and 3D domains that describe the corresponding subspaces in a sparse manner. We demonstrate the efficacy of our method by quantitative results on the 3D database and qualitative results on images drawn from the internet.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages3896-3900
Number of pages5
ISBN (Electronic)9781479983391
DOIs
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
Country/TerritoryCanada
CityQuebec City
Period9/27/159/30/15

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Keywords

  • 3D Model
  • Coupled Sparse Coding
  • Cross-modal Learning
  • Face Synthesis

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