Management of missing data in boosting cascades : application to face detection
This thesis has been realized in the ISPR group (ImageS, Perception systems and Robotics) of the Institut Pascal with the ComSee team (Computers that See). My research is involved in a project called Bio Rafale. It was created by the compagny Vesalis in 2008 and it is funded by OSEO. Its goal is to improve the security in stadium using identification of dangerous fans. The applications of these works deal with face detection. It is the first step in the process chain of the project. Most efficient detectors use a cascade of boosted classifiers. The term cascade refers to a sequential succession of several classifiers. The term boosting refers to a set of learning algorithms that linearly combine several weak classifiers. The detector selected for this thesis also uses a cascade of boosted classifiers. The training of such a cascade needs a training database and an image feature. Here, covariance matrices are used as image feature. The limits of an object detector are fixed by its training stage. One of our contributions is to adapt an object detector to handle some of its limits. The proposed adaptations lead to a problem of classification with missing data. A probabilistic formulation of a cascade is then used to incorporate the uncertainty introduced by the missing data. This formulation involves the estimation of a posteriori probabilities and the computation of new rejection thresholds at each level of the modified cascade. For these two problems, several solutions are proposed and extensive tests are done to find the best configuration. Finally, our solution is applied to the detection of turned or occluded faces using just an uprigth face detector. Detecting the turned faces requires the use of a 3D geometric model to adjust the position of the subwindow associated with each weak classifier.