| dc.contributor.author | Dürr, Oliver | en_US |
| dc.contributor.author | Pauchard, Yves | en_US |
| dc.contributor.author | Browarnik, Diego | en_US |
| dc.contributor.author | Axthelm, Rebekka | en_US |
| dc.contributor.author | Loeser, Martin | en_US |
| dc.contributor.editor | B. Solenthaler and E. Puppo | en_US |
| dc.date.accessioned | 2015-04-15T18:40:49Z | |
| dc.date.available | 2015-04-15T18:40:49Z | |
| dc.date.issued | 2015 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/egp.20151036 | en_US |
| dc.description.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 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | I.5.1 [Pattern Recognition] | en_US |
| dc.subject | Models | en_US |
| dc.subject | Neural Nets | en_US |
| dc.subject | I.5.2 [Pattern Recognition] | en_US |
| dc.subject | Design Methodology | en_US |
| dc.subject | Classifier Design and Evaluation | en_US |
| dc.subject | I.5.4 [Pattern Recognition] | en_US |
| dc.subject | Applications | en_US |
| dc.subject | Computer Vision | en_US |
| dc.title | Deep Learning on a Raspberry Pi for Real Time Face Recognition | en_US |
| dc.description.seriesinformation | EG 2015 - Posters | en_US |
| dc.description.sectionheaders | Posters | en_US |
| dc.identifier.doi | 10.2312/egp.20151036 | en_US |
| dc.identifier.pages | 11-12 | en_US |
