Face Recognition in the Wild

Face recognition is one of the most important tasks in pattern recognition and computer vision. The most conventional way to per- form face recognition is to compare a set of facial features that are extracted from a source image or a video frame with a reference image database of known faces. Such a classification takes the form of a prediction within a closed-set of classes. However, a more realistic scenario that fits the ground truth of real-world face recognition applications is to consider the possibility of encountering faces that do not belong to any of the training classes, i.e., an open-set classification. Such a constraint is very challenging to most existing face recognition systems since the latter are based on closed-set classification methods which always assign a training label to novel unknown instances even if they represent unseen faces that are not represented in the reference database. This results in a misclassification. In this paper, we introduce Face Recognition in the Wild (FRW), a novel face recognition system that allows (1) to efficiently recognize known faces from the reference database, and (2) to prevent misclassifying instances that represent unknown and unseen faces. FRW formulates this problem as a multi-class classification in an open-set context where the presence of instances from unknown classes is possible. Experimental results on the challenging Olivetti Faces benchmark dataset show the efficiency of our approach in open-set face recognition problems.