2D face pose normalisation using a 3D morphable model

Authors:
J. R. Tena;R. S. Smith;M. Hamouz;J. Kittler;A. Hilton;J. Illingworth
Affiliations:
Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, GU2 7NH, USA;Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, GU2 7NH, USA;Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, GU2 7NH, USA;Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, GU2 7NH, USA;Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, GU2 7NH, USA;Centre for Vision Speech and Signal Processing, University of Surrey, Guildford, GU2 7NH, USA
Venue:
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Year:
2007

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Pose-invariant face recognition by matching on multi-resolution MRFs linked by supercoupling transform

Computer Vision and Image Understanding

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Abstract

The ever growing need for improved security, surveillance and identity protection, calls for the creation of evermore reliable and robust face recognition technology that is scalable and can be deployed in all kinds of environments without compromising its effectiveness. In this paper we study the impact that pose correction has on the performance of 2D face recognition. To measure the effect, we use a state of the art 2D recognition algorithm. The pose correction is performed by means of 3D morphable model. Our results on the non frontal XM2VTS database showed that pose correction can improve recognition rates up to 30%.