Ear biometric database in the wild

Ear biometrics is gaining on popularity in recent years. One of the major problems in the domain is that there are no widely used, ear databases in the wild available. This makes comparison of existing ear recognition methods demanding and progress in the domain slower. Images that were taken under supervised conditions and are then used to train classifiers in ear recognition methods can in effect cause these classifiers classifiers to fail under application in the wild. In this paper we propose a new database which consists of ear images in the wild of known persons taken from the Internet. This ensures different indoor and outdoor lightning conditions, different viewing angles, occlusions, and a variety of image sizes and quality. In experiments we demonstrate that our database is more challenging than others.