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dc.contributor.authorBooth, J
dc.contributor.authorRoussos, A
dc.contributor.authorZafeiriou, S
dc.contributor.authorPonniah, A
dc.contributor.authorDunaway, D
dc.date.accessioned2018-03-08T10:56:11Z
dc.date.issued2016-12-12
dc.description.abstractWe present Large Scale Facial Model (LSFM) - a 3D Morphable Model (3DMM) automatically constructed from 9,663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM but also models tailored for specific age, gender or ethnicity groups. As an application example, we utilise the proposed model to perform age classification from 3D shape alone. Furthermore, we perform a systematic analysis of the constructed 3DMMs that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline. In addition, the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity are available on application to researchers involved in medically oriented research.en_GB
dc.description.sponsorshipJ. Booth is funded by an EPSRC DTA from Imperial College London, and holds a Qualcomm Innovation Fellowship. A. Roussos is funded by the Great Ormond Street Hospital Childrens Charity (Face Value: W1037). The work of S. Zafeiriou was partially funded by the EPSRC project EP/J017787/1 (4D-FAB).en_GB
dc.identifier.citation2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016, Las Vegas, NV, USA, pp. 5543 - 5552en_GB
dc.identifier.doi10.1109/CVPR.2016.598
dc.identifier.urihttp://hdl.handle.net/10871/31965
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.rights© 2016 IEEEen_GB
dc.subjectThree-dimensional displaysen_GB
dc.subjectSolid modelingen_GB
dc.subjectShapeen_GB
dc.subjectData modelsen_GB
dc.subjectPrincipal component analysisen_GB
dc.subjectTrainingen_GB
dc.subjectComputational modelingen_GB
dc.titleA 3D morphable model learnt from 10,000 facesen_GB
dc.typeConference paperen_GB
dc.date.available2018-03-08T10:56:11Z
dc.descriptionThis is the final version of the article. It is the open access version, provided by the Computer Vision Foundation. Except for the watermark, it is identical to the IEEE published version. Available from IEEE via the DOI in this record.en_GB


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