TY - GEN
T1 - Face Recognition Oak Ridge (FaRO)
T2 - 2020 IEEE/IAPR International Joint Conference on Biometrics, IJCB 2020
AU - Bolme, David S.
AU - Srinivas, Nisha
AU - Brogan, Joel
AU - Cornett, David
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/28
Y1 - 2020/9/28
N2 - The facial biometrics community has seen a recent abundance of high-accuracy facial analytic models become freely available. Although these models' capabilities in facial detection, landmark detection, attribute analysis, and recognition are ever-increasing, they aren't always straightforward to deploy in a real-world environment. In reality, the use of the field's ever growing collection of models is becoming exceedingly difficult as library dependencies update and deprecate. Researchers often encounter headaches when attempting to utilize multiple models requiring different or conflicting software packages. Face Recognition Oak Ridge (FaRO) is an open-source project designed to provide a highly modular, flexible framework for unifying facial analytic models through a compartmentalized plug-and-play paradigm built on top of the gRPC (Google Remote Procedure Call) protocol. FaRO's server-client architecture and flexible portability allows easy construction of modularized and heterogeneous face analysis pipelines, distributed over many machines with differing hardware and software resources. This paper outlines FaRO's architecture and current capabilities, along with some experiments in model testing and distributed scaling through FaRO.
AB - The facial biometrics community has seen a recent abundance of high-accuracy facial analytic models become freely available. Although these models' capabilities in facial detection, landmark detection, attribute analysis, and recognition are ever-increasing, they aren't always straightforward to deploy in a real-world environment. In reality, the use of the field's ever growing collection of models is becoming exceedingly difficult as library dependencies update and deprecate. Researchers often encounter headaches when attempting to utilize multiple models requiring different or conflicting software packages. Face Recognition Oak Ridge (FaRO) is an open-source project designed to provide a highly modular, flexible framework for unifying facial analytic models through a compartmentalized plug-and-play paradigm built on top of the gRPC (Google Remote Procedure Call) protocol. FaRO's server-client architecture and flexible portability allows easy construction of modularized and heterogeneous face analysis pipelines, distributed over many machines with differing hardware and software resources. This paper outlines FaRO's architecture and current capabilities, along with some experiments in model testing and distributed scaling through FaRO.
UR - http://www.scopus.com/inward/record.url?scp=85099695890&partnerID=8YFLogxK
U2 - 10.1109/IJCB48548.2020.9304933
DO - 10.1109/IJCB48548.2020.9304933
M3 - Conference contribution
AN - SCOPUS:85099695890
T3 - IJCB 2020 - IEEE/IAPR International Joint Conference on Biometrics
BT - IJCB 2020 - IEEE/IAPR International Joint Conference on Biometrics
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 September 2020 through 1 October 2020
ER -