Robust learning from normals for 3D face recognition

Marras, Ioannis and Zafeiriou, Stefanos and Tzimiropoulos, Georgios (2012) Robust learning from normals for 3D face recognition. Lecture Notes in Computer Science, 7584 . pp. 230-239. ISSN 0302-9743

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview


We introduce novel subspace-based methods for learning from the azimuth angle of surface normals for 3D face recognition. We show that the normal azimuth angles combined with Principal Component Analysis (PCA) using a cosine-based distance measure can be used for robust face recognition from facial surfaces. The proposed algorithms are well-suited for all types of 3D facial data including data produced by range cameras (depth images), photometric stereo (PS) and shade-from-X (SfX) algorithms. We demonstrate the robustness of the proposed algorithms both in 3D face reconstruction from synthetically occluded samples, as well as, in face recognition using the FRGC v2 3D face database and the recently collected Photoface database where the proposed method achieves state-of-the-art results. An important aspect of our method is that it can achieve good face recognition/verification performance by using raw 3D scans without any heavy preprocessing (i.e., model fitting, surface smoothing etc.).

Item Type: Article
Additional Information: The final publication is available at Springer via
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Tzimiropoulos, Yorgos
Date Deposited: 29 Jan 2016 13:25
Last Modified: 13 Sep 2016 15:18

Actions (Archive Staff Only)

Edit View Edit View