Coarse-to-fine support vector classifiers for face detection

We describe a new hierarchical face detection algorithm which allows fast background rejection in major parts of images and fine processing in area containing faces. This coarse-to-fine classification strategy is based on learning support vector classifiers (SVMs) with increasing evaluation complexity (resp. decreasing invariance and false alarm rates) top-down in the hierarchy. The complexity, in terms of the number of support vectors, of each detector in the hierarchy is reduced by clustering. We introduce the bias variation technique which allows each simplified SVM function to satisfy the conservation hypothesis as a criterion to get a consistent classifier in terms of detection rate, false alarms and background rejection efficiency. Face detection is performed using a depth-first search and cancel strategy which, for a given "face pattern", finds a root-leaf path with a sequence of positive answers.