Using k-nearest neighbors to handle missing weak classifiers in a boosted cascade

We propose a generic framework to handle missing weak classifiers at prediction time in a boosted cascade. The contribution is a probabilistic formulation of the cascade structure that considers the uncertainty introduced by missing weak classifiers. This new formulation involves two problems: 1) the approximation of posterior probabilities on each level and 2) the computation of thresholds on these probabilities to make a decision. Both problems are studied and solutions are proposed and evaluated. The method is then applied on a popular computer vision application: detecting occluded faces. Experimental results are provided on classic databases to evaluate the proposed solution related to the basic one.