Unsupervised learning of face detection models from unlabeled image streams
| dc.contributor.author | Walther, Thomas | |
| dc.contributor.author | Würtz, Rolf P. | |
| dc.contributor.editor | Brömme, Arslan | |
| dc.contributor.editor | Busch, Christoph | |
| dc.date.accessioned | 2018-11-19T13:16:36Z | |
| dc.date.available | 2018-11-19T13:16:36Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | Modern artificial face detection shows impressive performance in a variety of application areas. This success comes at the cost of supervised training, using large-scale databases provided by human experts. In this paper, we propose a face detection system based on Organic Computing [vdM08] paradigms that acquires necessary domain knowledge autonomously and learns a conceptual model of the human face/head region. Performance of the novel approach is experimentally compared to state-of-the-art face detection, yielding competitive results in scenarios of moderate complexity. | en |
| dc.identifier.isbn | 978-3-88579-290-1 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/18298 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BIOSIG 2012 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-196 | |
| dc.title | Unsupervised learning of face detection models from unlabeled image streams | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 231 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 221 | |
| gi.conference.date | 06.-07. September 2012 | |
| gi.conference.location | Darmstadt | |
| gi.conference.sessiontitle | Regular Research Papers |
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