Benchmarking Cross-Domain Face Recognition with Avatars, Caricatures and Sketches

The accuracy of face recognition skyrocketed in past years and its robustness towards various covariates has been shown, such as variations in pose or age. In recent years, a considerable amount of research efforts has been devoted to cross-domain face recognition aiming at comparing facial images obtained from domains that are different in capture technologies, e.g. visible spectrum versus infrared, or signal representations, e.g. photographs versus sketches. Yet, various relevant domains have hardly been explored and a lack of public databases hampers the development of new algorithms. In this work, we introduce the HDA Cross-Domain (HDA-CD) face image database comprising 1,400 face images from three different domains including avatars, caricatures, and sketches. Said face images were manually generated using popular mobile apps. In a benchmark, we evaluate commercial and open-source state-of-the-art facial analysis methods on the HDA-CD database including face detection and recognition. For the latter task, generated facial images are compared against their original counterparts. The HDA-CD database is made publicly available at: https://dasec.h-da.de/hda-cdfdb/