Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/15083
Title: Face and eye detection on hard datasets
Authors: Parris, Jon
Wilber, Michael
Heflin, Brian
Rara, Ham
El-barkouky, Ahmed
Farag, Aly
Movellan, Javier
Castrillón-Santana, Modesto 
Lorenzo Navarro, José Javier 
Teli, Mohammad Nayeem
Marcel, Sébastien
Atanasoaei, Cosmin
Boult, T.E.
UNESCO Clasification: 120304 Inteligencia artificial
Issue Date: 2011
Conference: 2011 International Joint Conference on Biometrics, IJCB 2011 
Abstract: Face and eye detection algorithms are deployed in a wide variety of applications. Unfortunately, there has been no quantitative comparison of how these detectors perform under difficult circumstances. We created a dataset of low light and long distance images which possess some of the problems encountered by face and eye detectors solving real world problems. The dataset we created is composed of re imaged images (photohead) and semi-synthetic heads im aged under varying conditions of low light, atmospheric blur, and distances of 3m, 50m, 80m, and 200m. This paper analyzes the detection and localization performance of the participating face and eye algorithms compared with the Viola Jones detector and four leading commercial face detectors. Performance is characterized under the different conditions and parameterized by per-image brightness and contrast. In localization accuracy for eyes, the groups/companies focusing on long-range face detection outperform leading commercial applications.
URI: http://hdl.handle.net/10553/15083
ISBN: 978-1-4577-1358-3
DOI: 10.1109/IJCB.2011.6117593
Source: 2011 International Joint Conference on Biometrics (IJCB), IEEE Biometrics Compendium [ISSN 2159-4244]
Appears in Collections:Actas de congresos
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