AdaBoost with totally corrective updates for fast face detection

An extension of the AdaBoost learning algorithm is proposed and brought to bear on the face detection problem. In each weak classifier selection cycle, the novel totally corrective algorithm reduces aggressively the upper bound on the training error by correcting coefficients of all weak classifiers. The correction steps are proven to lower the upper bound on the error without increasing computational complexity of the resulting detector. We show experimentally that for the face detection problem, where large training sets are available, the technique does not overfit. A cascaded face detector of the Viola-Jones type is built using AdaBoost with the totally corrective update. The same detection and false positive rates are achieved with a detector that is 20% faster and consists of only a quarter of the weak classifiers needed for a classifier trained by standard AdaBoost. The latter property facilitates hardware implementation, the former opens scope for the increease in the search space, e.g the range of scales at which faces are sought.