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

Brandon Amos, Bartosz Ludwiczuk, Mahadev Satyanarayanan
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213
CMU School of Computer Science, Technical Report CMU-CS-16-118, 2016

@techreport{amos2016openface,

   title={OpenFace: A general-purpose face recognition library with mobile applications},

   author={Amos, Brandon and Bartosz Ludwiczuk and Satyanarayanan, Mahadev},

   year={2016},

   institution={CMU-CS-16-118, CMU School of Computer Science}

}

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.
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OpenFace: A general-purpose face recognition library with mobile applications, 3.0 out of 5 based on 2 ratings

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