Personalized 3D-aided 2D facial landmark localization

Zhihong Zeng, Tianhong Fang, Shishir K. Shah, Ioannis A. Kakadiaris

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

2 Scopus citations


Facial landmark detection in images obtained under varying acquisition conditions is a challenging problem. In this paper, we present a personalized landmark localization method that leverages information available from 2D/3D gallery data. To realize a robust correspondence between gallery and probe key points, we present several innovative solutions, including: (i) a hierarchical DAISY descriptor that encodes larger contextual information, (ii) a Data-Driven Sample Consensus (DDSAC) algorithm that leverages the image information to reduce the number of required iterations for robust transform estimation, and (iii) a 2D/3D gallery pre-processing step to build personalized landmark metadata (i.e., local descriptors and a 3D landmark model). We validate our approach on the Multi-PIE and UHDB14 databases, and by comparing our results with those obtained using two existing methods.

Original languageEnglish (US)
Title of host publicationComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages14
EditionPART 2
StatePublished - 2011
Event10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: Nov 8 2010Nov 12 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6493 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th Asian Conference on Computer Vision, ACCV 2010
Country/TerritoryNew Zealand

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Personalized 3D-aided 2D facial landmark localization'. Together they form a unique fingerprint.

Cite this