BibTeX
@MISC{Levine_address:same,
author = {D. Levine and Ajit Rajwade and Mcconnell Engineering Building},
title = {Address: Same as above.},
year = {}
}
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Abstract
Face recognition from 3D shape data has been proposed as a method of biometric identification as a way of either supplementing or reinforcing a 2D 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 [2] 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 twostep 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 [8]. The latter step produces a residual error value, which can be used as a metric to
Keyphrases
frontal pose shape data approximate pose several pose coarse normalization face recognition support vector machine iterated closest point algorithm residual error value first step facial feature facial scan biometric identification iterative technique face recognition system unseen facial scan range image latter step support vector regression small set generic learning algorithm textural information generic learning strategy improved form hierarchical twostep strategy
