A fast method for training support vector machines with a very large set of linear features
Current systems for object detection often use support vector machines (SVM) as the basic classification algorithm. A rather common case is to compute a small set of linear features and then train the classifier on these features. We present a fast method to train and evaluate SVM with many linear features and show results for face detection using a set of 210400 features. The resulting classifier is both more accurate and faster than a classifier trained on raw pixel features, which total up only to 576 features in our case.