Multi-resolution 3D morphable models and its matching method

Bong Nam Kang, Hyeran Byun, Daijin Kim

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

3 Citations (Scopus)

Abstract

The inverse compositional image alignment (ICIA) is known as an efficient matching method for 3D morphable models (3DMMs). However, it requires a long computation time since the 3D face models consist of a large number of vertices. Also, it requires to recompute the Hessian matrix using the visible vertices every iteration. For a fast and an efficient matching, we propose the efficient and accurate hierarchical ICIA (HICIA) matching method for 3DMMs. The proposed matching method requires multi-resolution 3D face models and the Gaussian image pyramid. The multi-resolution 3D face models are built by sub-sampling at the 2:1 sampling rate to construct the lower-resolution 3D face models. For more accurate matching, we use a twostage model parameter update that only updates the rigid and the texture parameters and then updates all parameters after the initial convergence. We present several experimental results to prove that the proposed method shows better performance than that of the conventional ICIA matching method.

Original languageEnglish
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781424421756
DOIs
Publication statusPublished - 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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