UPM-GTI-Face: A dataset for the evaluation of the impact of distance and masks in face detection and recognition systems

Abstract

We present a novel dataset for the evaluation of face detection and recognition algorithms in challenging surveillance scenarios. The dataset consists in 4K images of different subjects captured at annotated distances ranging from 1 to 30 meters, both in indoor and outdoor environments, and under two face mask conditions (with and without). To the best of our knowledge, this is the only existing dataset that addresses the joint impact of masks and distances in a rigorous manner. We also propose an end-to-end fully automatic face detection and recognition system to provide baseline results on this dataset. Face detection is performed using Tiny Faces network, while face recognition is performed using VGG Face network. Experimental results show very high detection and recognition rates up to a distance of 20 meters, where the impact of distance is clear (especially for the latter). The use of face masks degrades the detection range and produces less consistent recognition results.