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

Conference · Tue Sep 01 00:00:00 EDT 2020

DOI:https://doi.org/10.1109/IJCB48548.2020.9304933· OSTI ID:1763493

Bolme, David  ^[1]; Srinivas, Nisha  ^[1]; Brogan, Joel  ^[1]; Cornett, David ^[1]

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1. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1763493
• Resource Relation: Conference: IEEE International Joint Conference on Biometrics (IJCB) - Houston, Texas, United States of America - 9/28/2020 8:00:00 AM-10/1/2020 8:00:00 AM
• Country of Publication: United States
• Language: English