Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Resampling Based Method for Pixel-wise Correspondence between 3D Faces
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Adaptive Rigid Multi-region Selection for Handling Expression Variation in 3D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Three-Dimensional Face Recognition Using Shapes of Facial Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition based on 3D ridge images obtained from range data
Pattern Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
Real-time 2D+3D facial action and expression recognition
Pattern Recognition
Bilinear Models for 3-D Face and Facial Expression Recognition
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Computer Vision and Image Understanding
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In this paper, we present a novel 3D face recognition algorithm based on the sparse representation. First, a 3D face normalization approach is proposed to deal with the raw faces. Then, three types of facial geometrical features are extracted to describe the 3D faces. Meanwhile, in order to guarantee the feasibility of the sparse representation framework and promote the recognition efficiency, a novel feature ranking scheme based on Fisher linear discriminant analysis (FLDA) is designed to arrange the facial descriptors. Finally, the sparse representation framework is used to collect all the face features, and it addresses the recognition task. The experiments tested on the BJUT-3D and FRGC v2.0 databases demonstrate the validity of the proposed 3D face recognition algorithm, and the necessity of the FLDA ranking scheme in the sparse representation framework.