| Face recognition from 3D shape information has been proposed as a method of biometric identification in recent times. This thesis presents a 3D face recognition system capable of recognizing the identity of an individual from his/her 3D facial scan in any pose across the view-sphere, by suitably comparing it with a set of models stored in a database. The system makes use of only 3D shape information ignoring textural information completely.; Firstly, the thesis proposes a generic learning strategy using support vector regression [11] to estimate the approximate pose of a 3D scan. The support vector machine (SVM) is trained on range images in several poses, belonging to a small set of individuals. This thesis also examines the relationship between size of the range image and the accuracy of the pose prediction from the scan.; Secondly, a hierarchical two-step strategy is proposed to normalize a facial scan to a nearly frontal pose before performing recognition. The first step consists of a coarse normalization making use of either the spatial relationships between salient 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 [17]. 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 outdo both 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 several existing 3D face recognition methods. |
