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Face Recognition Oak Ridge (FaRO): A Framework for Distributed and Scalable Biometrics Applications...

by David S Bolme, Nisha Srinivas, Joel R Brogan, David C Cornett
Publication Type
Conference Paper
Journal Name
International Joint Conference on Biometrics
Book Title
2020 IEEE International Joint Conference on Biometrics (IJCB)
Publication Date
Page Numbers
1 to 8
Conference Name
IEEE International Joint Conference on Biometrics (IJCB)
Conference Location
Houston, Texas, United States of America
Conference Sponsor
Conference Date

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.