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Face recognition using principal geodesic analysis and manifold learning

Dickens, M. P., Smith, W. A. P., Wu, Jing ORCID: and Hancock, E. R . 2007. Face recognition using principal geodesic analysis and manifold learning. Presented at: Third Iberian Conference, IbPRIA 2007, Girona, Spain, 6-8 June 2007. Published in: Martí, J. ed. Pattern Recognition and Image Analysis: Third Iberian Conference, IbPRIA 2007, Girona, Spain, June 6-8, 2007, Proceedings, Part I. Lecture Notes in Computer Science , vol.4477 (4477) Berlin Heidelberg: Springer, pp. 426-434. 10.1007/978-3-540-72847-4_55

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This paper describes how face recognition can be effected using 3D shape information extracted from single 2D image views. We characterise the shape of the field of facial normals using a statistical model based on principal geodesic analysis. The model can be fitted to 2D brightness images of faces to recover a vector of shape parameters. Since it captures variations in a field of surface normals, the dimensionality of the shape vector is twice the number of image pixels. We investigate how to perform face recognition using the output of PGA by applying a number of dimensionality reduction techniques including principal components analysis, locally linear embedding, locality preserving projection and Isomap.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Springer
ISBN: 9783540728467
ISSN: 0302-9743
Last Modified: 01 Nov 2022 10:00

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