Computer Science Department
School of Computer Science, Carnegie Mellon University


OpenFace: A General-purpose Face Recognition Library
with Mobile Applications

Brandon Amos, Bartosz Ludwiczuk*, Mahadev Satyanarayanan

June 2016


Keywords: Face recognition, deep learning, machine learning, computer vision, neural networks, mobile computing

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:

20 pages

*Poznan University of Technology

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