Chris Bensing Boehnen
Oak Ridge National Laboratory
22 Papers
225 Citations
Chris Bensing Boehnen is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Iris recognition & Biometrics. The author has an hindex of 9, co-authored 22 publications. Previous affiliations of Chris Bensing Boehnen include University of Notre Dame.
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Papers
3D Face Recognition Using 3D Alignment for PCA
T.D. Russ,Chris Bensing Boehnen,T. Peters +2 more
- 17 Jun 2006
TL;DR: The approach addresses the issue of proper 3D face alignment required by PCA for maximum data compression and good generalization performance for new untrained faces by achieving correspondence of facial points by registering a3D face to a scaled generic 3D reference face and subsequently perform a surface normal search algorithm.
94
The Effect of Decomposition on the Efficacy of Biometrics for Positive Identification.
TL;DR: While this study is an initial step in determining the utility of physiological biometrics across postmortem time, biometric research has the potential to make important contributions to human identification and the law enforcement, military, and medicolegal communities.
46
Impact of environmental factors on biometric matching during human decomposition
David S. Bolme,Ryan Tokola,Chris Bensing Boehnen,Tiffany B. Saul,Kelly A. Sauerwein,Dawnie Wolfe +5 more
- 01 Sep 2016
TL;DR: This paper discusses a multimodal dataset of finger-prints, faces, and irises from twelve donated human subjects that decomposed outdoors under natural conditions and includes predictive models relating time and temperature, measured as Accumulated Degree Days (ADD), and season, to the probability of automatic verification using a commercial algorithm.
36
ORNL biometric eye model for iris recognition
Hector J. Santos-Villalobos,Del R. Barstow,Mahmut Karakaya,Chris Bensing Boehnen,Edward Chaum +4 more
- 06 Dec 2012
TL;DR: A new biometric targeted eye model and a method to reconstruct the off-axis eye to its frontal view allowing for recognition using existing methods and algorithms to be largely unmodified by using this work as a pre-processor to improve performance.
31
3D Signatures for Fast 3D Face Recognition
Chris Bensing Boehnen,Tanya Peters,Patrick J. Flynn +2 more
- 04 Jun 2009
TL;DR: The proposed 3D face recognition method employing the 3D signature ran more than three orders of magnitude faster than a traditional ICP based distance implementation, without sacrificing accuracy, and improves biometric performance over simple spherical cut regions used previously.