| Publication type: | Conference poster |
| Type of review: | Not specified |
| Title: | Deep learning on a Raspberry Pi for real time face recognition |
| Authors: | Dürr, Oliver Pauchard, Yves Browarnik, Diego Hernan Axthelm, Rebekka Loeser, Martin |
| DOI: | 10.2312/egp.20151036 |
| Proceedings: | EG 2015 - Posters |
| Page(s): | 11 |
| Pages to: | 12 |
| Conference details: | Eurographics Conference (EG 2015), Zurich, 4-8 May 2015 |
| Issue Date: | 2015 |
| Publisher / Ed. Institution: | The Eurographics Association |
| Language: | English |
| Subjects: | Face recognition; Raspberry Pi; Deep learning |
| Subject (DDC): | 006: Special computer methods |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/13891 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Applied Information Technology (InIT) Institute of Data Analysis and Process Design (IDP) Institute of Signal Processing and Wireless Communications (ISC) |
| Appears in collections: | Publikationen School of Engineering |
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