caa a22 4500 467936943 CHVBK 20180406153000.0 cr unu---uuuuu 170328e20061001xx s 000 0 eng 10.1007/s00138-006-0036-0 doi (NATIONALLICENCE)springer-10.1007/s00138-006-0036-0 Three-dimensional view-invariant face recognition using a hierarchical pose-normalization strategy [Elektronische Daten] [Martin Levine, Ajit Rajwade] 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. Springer-Verlag, 2006 3D face recognition nationallicence Support vector regression nationallicence Iterated closest point nationallicence 3D pose estimation nationallicence Pose normalization nationallicence Residual error nationallicence Levine Martin Center of Intelligent Machines, McGill University, Room 410, 3480 University Street, McConnell Engineering Building, H3A 2A7, Montreal, Canada aut Rajwade Ajit Center of Intelligent Machines, McGill University, Room 410, 3480 University Street, McConnell Engineering Building, H3A 2A7, Montreal, Canada aut Machine Vision and Applications Springer-Verlag 17/5(2006-10-01), 309-325 0932-8092 17:5<309 2006 17 138 https://doi.org/10.1007/s00138-006-0036-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00138-006-0036-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Levine Martin Center of Intelligent Machines, McGill University, Room 410, 3480 University Street, McConnell Engineering Building, H3A 2A7, Montreal, Canada aut NATIONALLICENCE 700 1- Rajwade Ajit Center of Intelligent Machines, McGill University, Room 410, 3480 University Street, McConnell Engineering Building, H3A 2A7, Montreal, Canada aut NATIONALLICENCE 773 0- Machine Vision and Applications Springer-Verlag 17/5(2006-10-01), 309-325 0932-8092 17:5<309 2006 17 138 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer