AgeDB: The First Manually Collected, In-The-Wild Age Database

Stylianos Moschoglou, Athanasios Papaioannou, Christos Sagonas, Jiankang Deng, Irene Kotsia, Stefanos Zafeiriou; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 51-59

Over the last few years, increased interest has arisen with respect to age-related tasks in the Computer Vision community. As a result, several "in-the-wild" databases annotated with respect to the age attribute became available in the literature. Nevertheless, one major drawback of these databases is that they are semi-automatically collected and annotated and thus they contain noisy labels. Therefore, the algorithms that are evaluated in such databases are prone to noisy estimates. In order to overcome such drawbacks, we present in this paper the first, to the best of knowledge, manually collected "in-the-wild" age database, dubbed AgeDB, containing images annotated with accurate to the year, noise-free labels. As demonstrated by a series of experiments utilizing state-of-the-art algorithms, this unique property renders AgeDB suitable when performing experiments on age-invariant face verification, age estimation and face age progression "in-the-wild".