Finding objects and faces with a cascade of Haar features

We learned in the previous recipe, some of the basic concepts of machine learning. We showed how a classifier can be built by collecting samples of the different classes of interest. However, for the approach that was considered in this previous recipe, training a classifier simply consists of storing all the samples' representations. From there, the label of any new instance can be predicted by looking at the closest (nearest neighbor) labeled point. For most machine learning methods, training is rather an iterative process during which machinery is built by looping over the samples. Performance of the classifier thus produced gradually improves as more samples are presented. Learning eventually stops when a certain performance criterion is reached or when no more improvements can be obtained by considering the current training dataset. This recipe will present a machine learning algorithm that follows this procedure, the cascade...