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CVIU
2007

Multi-view face and eye detection using discriminant features

13 years 3 months ago
Multi-view face and eye detection using discriminant features
Multi-view face detection plays an important role in many applications. This paper presents a statistical learning method to extract features and construct classifiers for multi-view face detection. Specifically, a recursive nonparametric discriminant analysis (RNDA) method is presented. The RNDA relaxes Gaussian assumptions of Fisher discriminant analysis (FDA), and it can handle more general class distributions. RNDA also improves the traditional nonparametric discriminant analysis (NDA) by alleviating its computational complexity. The resulting RNDA features provide better accuracy than the commonly used Haar features in detecting objects of complex shapes. Histograms of extracted features are learned to represent class distributions and to construct probabilistic classifiers. RNDA features are subsequently learned and combined with AdaBoost to form a multi-view face detector. The method is applied to both multiview face and eye detection, and experimental results demonstrate im...
Peng Wang, Qiang Ji
Added 18 Dec 2010
Updated 18 Dec 2010
Type Journal
Year 2007
Where CVIU
Authors Peng Wang, Qiang Ji
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