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.

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Amos, B., Ludwiczuk, B., Satyanarayanan, M.
Technical Report CMU-CS-16-118, Computer Science Department, Carnegie Mellon University, June 2016