3D face alignment without correspondences

Zsolt Sánta, Z. Kato

Research output: Conference contribution

4 Citations (Scopus)

Abstract

A novel correspondence-less approach is proposed to find a thin plate spline map between a pair of 3D human faces represented by triangular surface meshes. The proposed method works without landmark extraction and feature correspondences. The aligning transformation is simply found by solving a system of nonlinear equations. Each equation is generated by integrating a non-linear function over the surfaces represented as fuzzy sets of triangles. We derive approximating formulas for the efficient computation of these integrals. Based on a series of comparative tests on a standard 3D face dataset, our triangular mesh-based algorithm outperforms state of the art methods in terms of computing time while maintaining accuracy.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2016 Workshops, Proceedings
PublisherSpringer Verlag
Pages521-535
Number of pages15
Volume9914 LNCS
ISBN (Print)9783319488806
DOIs
Publication statusPublished - 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: okt. 8 2016okt. 16 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9914 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other14th European Conference on Computer Vision, ECCV 2016
CountryNetherlands
CityAmsterdam
Period10/8/1610/16/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Sánta, Z., & Kato, Z. (2016). 3D face alignment without correspondences. In Computer Vision – ECCV 2016 Workshops, Proceedings (Vol. 9914 LNCS, pp. 521-535). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9914 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-48881-3_36