Paper Info
Title
A Prescreener for 3D Face Recognition Using Radial Symmetry and the Hausdorff Fraction
Abstract
Face recognition systems require the ability to efficiently scan an existing database of faces to locate a match for a newly acquired face. The large number of faces in real world databases makes computationally intensive algorithms impractical for scanning entire databases. We propose the use of more efficient algorithms to "prescreen" face databases, determining a limited set of likely matches that can be processed further to identify a match. We use both radial symmetry and shape to extract five features of interest on 3D range images of faces. These facial features determine a very small subset of discriminating points which serve as input to a prescreening algorithm based on a Hausdorff fraction. We show how to compute the Haudorff fraction in linear O(n) time using a range image representation. Our feature extraction and prescreening algorithms are verified using the FRGC v1.0 3D face scan data. Results show 97% of the extracted facial features are within 10 mm or less of manually marked ground truth, and the prescreener has a rank 6 recognition rate of 100%.
Year
DOI
Venue
2005
10.1109/CVPR.2005.566
CVPR Workshops
Field
DocType
Volume
Computer vision,Facial recognition system,Pattern recognition,Computer science,Image representation,Symmetry in biology,Feature extraction,Ground truth,Artificial intelligence,Hausdorff space
Conference
2005
Issue
ISSN
ISBN
1
2160-7508
0-7695-2372-2-3
Citations 
PageRank 
References 
15
1.17
11
Authors
3
Name
Order
Citations
PageRank
Melissa L. Koudelka1775.49
Mark W. Koch29210.60
Trina D. Russ3462.71