Automatic Semantic Face Recognition

Recent expansion in surveillance systems has motivated research in soft biometrics that enable the unconstrained recognition of human faces. Comparative soft biometrics show superior recognition performance than categorical soft biometrics and have been the focus of several studies which have highlighted their ability for recognition and retrieval in constrained and unconstrained environments. These studies, however, only addressed face recognition for retrieval using human generated attributes, posing a question about the feasibility of automatically generating comparative labels from facial images. In this paper, we propose an approach for the automatic comparative labelling of facial soft biometrics. Furthermore, we investigate unconstrained human face recognition using these comparative soft biometrics in a human labelled gallery (and vice versa). Using a subset from the LFW dataset, our experiments show the efficacy of the automatic generation of comparative facial labels, highlighting the potential extensibility of the approach to other face recognition scenarios and larger ranges of attributes.