An effective approach to pose invariant 3D face recognition

Authors:
Dayong Wang;Steven C. H. Hoi;Ying He
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Year:
2011

Citing 13
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
A Survey Of Approaches To Three-Dimensional Face Recognition

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Smooth minimization of non-smooth functions

Mathematical Programming: Series A and B
Face recognition from a single image per person: A survey

Pattern Recognition
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

The Journal of Machine Learning Research
2D and 3D face recognition: A survey

Pattern Recognition Letters
Tied Factor Analysis for Face Recognition across Large Pose Differences

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Surface Parameterization Using Inverse Curvature Map

IEEE Transactions on Visualization and Computer Graphics
Robust Face Recognition via Sparse Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition across pose: A review

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

One critical challenge encountered by existing face recognition techniques lies in the difficulties of handling varying poses. In this paper, we propose a novel pose invariant 3D face recognition scheme to improve regular face recognition from two aspects. Firstly, we propose an effective geometry based alignment approach, which transforms a 3D face mesh model to a well-aligned 2D image. Secondly, we propose to represent the facial images by a Locality Preserving Sparse Coding (LPSC) algorithm, which is more effective than the regular sparse coding algorithm for face representation. We conducted a set of extensive experiments on both 2D and 3D face recognition, in which the encouraging results showed that the proposed scheme is more effective than the regular face recognition solutions.