Robust 3D face recognition from expression categorisation

Jamie Cook, Mark Cox, Vinod Chandran, Sridha Sridharan

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

8 Scopus citations


The task of Face Recognition is often cited as being complicated by the presence of lighting and expression variation. In this article a novel combination of facial expression categorisation and 3D Face Recognition is used to provide enhanced recognition performance. The use of 3D face data alleviates performance issues related to pose and illumination. Part-face decomposition is combined with a novel adaptive weighting scheme to increase robustness to expression variation. By using local features instead of a monolithic approach, this system configuration allows for expression variability to be modelled and aid in the fusion process. The system is tested on the Face Recognition Grand Challenge (FRGC) database, currently the largest available dataset of 3D faces. The sensitivity of the proposed approach is also evaluated in the presence of systematic error in the expression classification stage.

Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2007, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783540745488
StatePublished - 2007
Event2007 International Conference on Advances in Biometrics, ICB 2007 - Seoul, Korea, Republic of
Duration: Aug 27 2007Aug 29 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4642 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2007 International Conference on Advances in Biometrics, ICB 2007
Country/TerritoryKorea, Republic of


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