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Title: 3D face analysis for demographic biometrics

Abstract

Despite being increasingly easy to acquire, 3D data is rarely used for face-based biometrics applications beyond identification. Recent work in image-based demographic biometrics has enjoyed much success, but these approaches suffer from the well-known limitations of 2D representations, particularly variations in illumination, texture, and pose, as well as a fundamental inability to describe 3D shape. This paper shows that simple 3D shape features in a face-based coordinate system are capable of representing many biometric attributes without problem-specific models or specialized domain knowledge. The same feature vector achieves impressive results for problems as diverse as age estimation, gender classification, and race classification.

Authors:
 [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
1214479
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Conference on Biometrics (ICB), Phuket, Thailand, 20150519, 20150522
Country of Publication:
United States
Language:
English

Citation Formats

Tokola, Ryan A, Mikkilineni, Aravind K, and Boehnen, Chris Bensing. 3D face analysis for demographic biometrics. United States: N. p., 2015. Web.
Tokola, Ryan A, Mikkilineni, Aravind K, & Boehnen, Chris Bensing. 3D face analysis for demographic biometrics. United States.
Tokola, Ryan A, Mikkilineni, Aravind K, and Boehnen, Chris Bensing. Thu . "3D face analysis for demographic biometrics". United States. doi:. https://www.osti.gov/servlets/purl/1214479.
@article{osti_1214479,
title = {3D face analysis for demographic biometrics},
author = {Tokola, Ryan A and Mikkilineni, Aravind K and Boehnen, Chris Bensing},
abstractNote = {Despite being increasingly easy to acquire, 3D data is rarely used for face-based biometrics applications beyond identification. Recent work in image-based demographic biometrics has enjoyed much success, but these approaches suffer from the well-known limitations of 2D representations, particularly variations in illumination, texture, and pose, as well as a fundamental inability to describe 3D shape. This paper shows that simple 3D shape features in a face-based coordinate system are capable of representing many biometric attributes without problem-specific models or specialized domain knowledge. The same feature vector achieves impressive results for problems as diverse as age estimation, gender classification, and race classification.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

Conference:
Other availability
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