Recognition of partially occluded and/or imprecisely localized faces using a probabilistic approach

New face recognition approaches are needed, because although much progress has been recently achieved in the field (e.g. within the eigenspace domain), still many problems are to be robustly solved. Two of these problems are occlusions and the imprecise localization of faces (which ultimately imply a failure in identification). While little has been done to account for the first problem, almost nothing has been proposed to account for the second. This paper presents a probabilistic approach that attempts to solve both problems while using an eigenspace representation. To resolve the localization problem, we need to find the subspace (within the feature space, e.g. eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into n local regions which are analyzed in isolation. In contrast with other previous approaches, where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. Our method has proven to be superior to a local voting PCA on a set of 2600 face images.