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Title: A 2D range Hausdorff approach for 3D face recognition.

Abstract

This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and template datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.

Authors:
; ;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
970204
Report Number(s):
SAND2005-2316C
TRN: US201003%%431
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the Face Recognition Grand Challenge Workshop at CVPR 2005 held June 20-26, 2005 in San Diego, CA.
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ALGORITHMS; ALIGNMENT; PERFORMANCE; PROBES; STORAGE; HAUSDORFF SPACE

Citation Formats

Koch, Mark William, Russ, Trina Denise, and Little, Charles Quentin. A 2D range Hausdorff approach for 3D face recognition.. United States: N. p., 2005. Web.
Koch, Mark William, Russ, Trina Denise, & Little, Charles Quentin. A 2D range Hausdorff approach for 3D face recognition.. United States.
Koch, Mark William, Russ, Trina Denise, and Little, Charles Quentin. Fri . "A 2D range Hausdorff approach for 3D face recognition.". United States. doi:.
@article{osti_970204,
title = {A 2D range Hausdorff approach for 3D face recognition.},
author = {Koch, Mark William and Russ, Trina Denise and Little, Charles Quentin},
abstractNote = {This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and template datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Apr 01 00:00:00 EST 2005},
month = {Fri Apr 01 00:00:00 EST 2005}
}

Conference:
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