Robust learning from normals for 3D face recognition

Marras, Ioannis; Zafeiriou, Stefanos; Tzimiropoulos, Georgios

Abstract

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.).

Citation

Marras, I., Zafeiriou, S., & Tzimiropoulos, G. (2012). Robust learning from normals for 3D face recognition. Lecture Notes in Artificial Intelligence, 7584, https://doi.org/10.1007/978-3-642-33868-7_23

• Journal Article Type: Article
• Conference Name: Computer Vision--ECCV 2012. Workshops and Demonstrations
• End Date: Oct 13, 2012
• Publication Date: Jan 1, 2012
• Deposit Date: Jan 29, 2016
• Publicly Available Date: Jan 29, 2016
• Journal: Lecture Notes in Computer Science
• Electronic ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 7584
• DOI: https://doi.org/10.1007/978-3-642-33868-7_23
• Public URL: https://nottingham-repository.worktribe.com/output/1008895
• Publisher URL: http://link.springer.com/chapter/10.1007%2F978-3-642-33868-7_23
• Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33868-7_23