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Unsupervised learning of face detection models from unlabeled image streams

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2012

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Gesellschaft für Informatik e.V.

Zusammenfassung

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.

Beschreibung

Walther, Thomas; Würtz, Rolf P. (2012): Unsupervised learning of face detection models from unlabeled image streams. BIOSIG 2012. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-290-1. pp. 221-231. Regular Research Papers. Darmstadt. 06.-07. September 2012

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