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|Title:||Face detection with effective feature extraction|
|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 Place:||Heidelberger Platz 3 Berlin Germany D-14197|
|Series/Report no.:||Lecture notes on Computer Science ; 6495|
|Conference Name:||Asian Conference on Computer Vision (10th : 2010 : Queenstown, New Zealand)|
|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|
|Appears in Collections:||Computer Science publications|
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