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 multi-view face and eye detection, and experimental results demonstrate improved performance over existing methods.