Learning features from examples for face detection

In this paper, the linear support vector machine (LPSVM) algorithm is used to construct an over complete set of weak classifiers, and AdaBoost algorithm are adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method can minimize the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learnt from the training set instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs well.