Face Detection with Mixtures of Boosted Discriminant Features
Detecting faces in images is a key step in numerous computer vision applications as face recognition for example. Face detection is a difficult task in image analysis because of the large face intra-class variability which is due to the important influence of the environmental conditions on the face aspect. The existing methods for face detection can be divided into holistic methods and feature based methods. We propose a new method for detecting frontal faces in complex images featuring two main contributions: the use of a collection of highly discriminative anisotropic Gaussian features combined by boosting and the computation using a mixture of classifiers to improve the classification capabilities without affecting the detection speed. The performances of the face detector have been evaluated on the CMU/MIT test set  database. This methods outperforms the previous works in frontal face detection.