Title :
A multi-sample standoff multimodal biometric system
Author :
Boehnen, Chris ; Barstow, Del ; Patlolla, Dilip ; Mann, Chris
Author_Institution :
Imaging, Signals, & Machine Learning Group, Oak Ridge Nat. Lab., Oak Ridge, TN, USA
Abstract :
The data captured by existing standoff biometric systems typically has lower biometric recognition performance than their close range counterparts due to imaging challenges, pose challenges, and other factors. To assist in overcoming these limitations systems typically perform in a multi-modal capacity such as Honeywell´s Combined Face and Iris (CFAIRS) [21] system. While this improves the systems performance, standoff systems have yet to be proven as accurate as their close range equivalents. We will present a standoff system capable of operating up to 7 meters in range. Unlike many systems such as the CFAIRS our system captures high quality 12 MP video allowing for a multi-sample as well as multimodal comparison. We found that for standoff systems multi-sample improved performance more than multimodal. For a small test group of 50 subjects we were able to achieve 100% rank one recognition performance with our system on standoff recognition of noncooperative subjects.
Keywords :
face recognition; iris recognition; CFAIRS; Honeywell Combined Face and Iris system; biometric recognition; multimodal capacity; multisample standoff multimodal biometric system; noncooperative subjects; standoff recognition; Cameras; Face; Iris recognition; Lenses; Lighting; Portals; Throughput; biometric identification; multimodal biometrics; standoff face recognition; standoff iris recognition;
Conference_Titel :
Biometrics: Theory, Applications and Systems (BTAS), 2012 IEEE Fifth International Conference on
Conference_Location :
Arlington, VA
Print_ISBN :
978-1-4673-1384-1
Electronic_ISBN :
978-1-4673-1383-4
DOI :
10.1109/BTAS.2012.6374567