Deep Learning on a Raspberry Pi for Real Time Face Recognition

Dürr, Oliver; Pauchard, Yves; Browarnik, Diego; Axthelm, Rebekka; Loeser, Martin

Deep Learning on a Raspberry Pi for Real Time Face Recognition

Files

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poster1011_poster.pdf (1.16 MB)

Date

2015

Authors

Dürr, Oliver
Pauchard, Yves
Browarnik, Diego
Axthelm, Rebekka
Loeser, Martin

Publisher

The Eurographics Association

Abstract

In this paper we describe a fast and accurate pipeline for real-time face recognition that is based on a convolutional neural network (CNN) and requires only moderate computational resources. After training the CNN on a desktop PC we employed a Raspberry Pi, model B, for the classification procedure. Here, we reached a performance of approximately 2 frames per second and more than 97% recognition accuracy. The proposed approach outperforms all of OpenCV's algorithms with respect to both accuracy and speed and shows the applicability of recent deep learning techniques to hardware with limited computational performance

        @inproceedings{10.2312:egp.20151036
,
booktitle = {EG 2015 - Posters
},
editor = {B. Solenthaler and E. Puppo
},
title = {{Deep Learning on a Raspberry Pi for Real Time Face Recognition
}},
author = {Dürr, Oliver and 
Pauchard, Yves and 
Browarnik, Diego and 
Axthelm, Rebekka and 
Loeser, Martin
},
year = {2015
},
publisher = {The Eurographics Association
},
ISBN = {},
DOI = {10.2312/egp.20151036
}
}