Face recognition from range data by structural analysis
Syntactic and structural pattern recognition
Face recognition from depth and curvature
Face recognition from depth and curvature
The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A compact algorithm for rectification of stereo pairs
Machine Vision and Applications
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Scattered Data Interpolation with Multilevel B-Splines
IEEE Transactions on Visualization and Computer Graphics
Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Face Recognition Using Range Images
VSMM '97 Proceedings of the 1997 International Conference on Virtual Systems and MultiMedia
Face recognition with Multilevel B-Splines and Support Vector Machines
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
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In this paper a new complete system for 3D face recognition is presented. 3D face recognition presents several advantages against 2D face recognition, as, for example, invariance to illumination conditions. The proposed system makes use of a stereo methodology, that does not require any expensive range sensors. The 3D image of the face is modelled using Multilevel B-Splines coefficients, that are classified using Support Vector Machines. Preliminary experimental evaluation has produced encouraging results, making the proposed system a promising low cost 3D face recognition system.