Computational geometry: an introduction
Computational geometry: an introduction
K-d trees for semidynamic point sets
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Rigid, affine and locally affine registration of free-form surfaces
International Journal of Computer Vision
Face and Gesture Recognition: Overview
IEEE Transactions on Pattern Analysis and Machine Intelligence
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the sixth ACM symposium on Solid modeling and applications
Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Use of depth and colour eigenfaces for face recognition
Pattern Recognition Letters
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
3D Human Face Recognition Using Point Signature
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Support Vector Regression and Classification Based Multi-View Face Detection and Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Using Range Images
VSMM '97 Proceedings of the 1997 International Conference on Virtual Systems and MultiMedia
Registration and Integration of Multiple Range Images for 3-D Model Construction
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Three-Dimensional Model Based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Face recognition from 2D and 3D images using 3D Gabor filters
Image and Vision Computing
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Face recognition from three-dimensional (3D) shape data has been proposed as a method of biometric identification as a way of either supplanting or reinforcing a two-dimensional approach. This paper presents a 3D face recognition system capable of recognizing the identity of an individual from a 3D facial scan in any pose across the view-sphere, by suitably comparing it with a set of models (all in frontal pose) stored in a database. The system makes use of only 3D shape data, ignoring textural information completely. Firstly, we propose a generic learning strategy using support vector regression [Burges, Data Mining Knowl Discov 2(2): 121–167, 1998] to estimate the approximate pose of a 3D head. The support vector machine (SVM) is trained on range images in several poses belonging to only a small set of individuals and is able to coarsely estimate the pose of any unseen facial scan. Secondly, we propose a hierarchical two-step strategy to normalize a facial scan to a nearly frontal pose before performing any recognition. The first step consists of either a coarse normalization making use of facial features or the generic learning algorithm using the SVM. This is followed by an iterative technique to refine the alignment to the frontal pose, which is basically an improved form of the Iterated Closest Point Algorithm [Besl and Mckay, IEEE Trans Pattern Anal Mach Intell 14(2):239–256, 1992]. The latter step produces a residual error value, which can be used as a metric to gauge the similarity between two faces. Our two-step approach is experimentally shown to outperform both of the individual normalization methods in terms of recognition rates, over a very wide range of facial poses. Our strategy has been tested on a large database of 3D facial scans in which the training and test images of each individual were acquired at significantly different times, unlike all except two of the existing 3D face recognition methods.