Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/65344
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Type: Conference paper
Title: Face detection with effective feature extraction
Author: Paisitkriangkrai, S.
Shen, C.
Zhang, J.
Citation: Computer Vision - ACCV 2010: Proceedings of 10th Asian Conference on Computer Vision, held in Queenstown, New Zealand, Nov 8-12 2010, revised selected papers, part 1 / R. Kimmel, R. Klette and A. Sugimoto (eds.): pp.460-470
Publisher: Springer-Verlag Berlin
Publisher Place: Heidelberger Platz 3 Berlin Germany D-14197
Issue Date: 2011
Series/Report no.: Lecture notes on Computer Science ; 6495
ISBN: 9783642193170
ISSN: 0302-9743
1611-3349
Conference Name: Asian Conference on Computer Vision (10th : 2010 : Queenstown, New Zealand)
Statement of
Responsibility: 
Sakrapee Paisitkriangkrai, Chunhua Shen and Jian Zhang
Abstract: There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.
Keywords: Face detection, boosting, Haar features, histogram of oriented gradients, feature co-occurrence
Rights: Copyright Springer-Verlag Berlin Heidelberg 2011
RMID: 0020111774
DOI: 10.1007/978-3-642-19318-7_36
Appears in Collections:Computer Science publications

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