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Surface normals based landmarking for 3D face recognition using photometric stereo captures

Gao, Jiangning; Hansen, Mark; Smith, Melvyn; Evans, Adrian N.

Authors

Jiangning Gao

Mark Hansen Mark.Hansen@uwe.ac.uk
Professor of Machine Vision and Machine Learning

Melvyn Smith

Adrian N. Evans



Abstract

In recent decades, many 3D data acquisition methods have been developed to provide accurate and cost-effective 3D captures of the human face. An example system, which can accommodate both research and commercial applications, is the Photoface device. Photoface is based on the photometric stereo imaging technique. To improve the recognition performance using Photoface captures, a novel landmarking algorithm is first proposed by thresholding surface normals maps. The development of landmarking algorithms specifically for photometric stereo captures enables region-based feature extraction and fills a gap in the 3D face landmarking literature. Nasal curves and spherical patches are then used respectively for recognition and are evaluated on the 3DE-VISIR database, which contains Photoface captures with expressions. The neutral vs. non-neutral matching results demonstrate high face recognition performance using spherical patches and a KFA classifier, achieving a R RR of 97.26% when only 24 patches are selected for matching. 1

Citation

Gao, J., Hansen, M., Smith, M., & Evans, A. N. (2019). Surface normals based landmarking for 3D face recognition using photometric stereo captures. In Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications. , (43-47). https://doi.org/10.1145/3345336.3345339

Conference Name 2019 3rd International Conference on Biometric Engineering and Applications
Conference Location Stockholm, Sweden
Start Date May 29, 2019
End Date May 31, 2019
Acceptance Date Apr 10, 2018
Publication Date May 1, 2019
Deposit Date Apr 30, 2021
Pages 43-47
Book Title Proceedings of the 2019 3rd International Conference on Biometric Engineering and Applications
ISBN 9781450363051
DOI https://doi.org/10.1145/3345336.3345339
Public URL https://uwe-repository.worktribe.com/output/7319788