OpenFace: A general-purpose face recognition library with mobile applications

Cameras are becoming ubiquitous in the Internet of Things (IoT) and can use face recognition technology to improve context. There is a large accuracy gap between today’s publicly available face recognition systems and the state-of-the-art private face recognition systems. This paper presents our OpenFace face recognition library that bridges this accuracy gap. We show that OpenFace provides near-human accuracy on the LFW benchmark and present a new classification benchmark for mobile scenarios. This paper is intended for non-experts interested in using OpenFace and provides a light introduction to the deep neural network techniques we use. We released OpenFace in October 2015 as an open source library under the Apache 2.0 license. It is available at: http://cmusatyalab.github.io/openface/ This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by Crown Castle, the Conklin Kistler family fund, Google, the Intel Corporation, and Vodafone. NVIDIA’s academic hardware grant provided the Tesla K40 GPU used in all of our experiments. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources.