Extended YouTube Faces: a Dataset for Heterogeneous Open-Set Face Identification

In this paper, we propose an extension of the famous YouTube Faces (YTF) dataset. In the YTF dataset, the goal was to state whether two videos contained the same subject or not (video-based face verification). We enrich YTF with still images and an identification protocol. In the classic face identification, given a probe image (or video), the correct identity has to be retrieved among the gallery ones; the main peculiarity of such protocol is that each probe identity has a correspondent in the gallery (\textit{closed-set}). To resemble a realistic and practical scenario, we devised a protocol in which probe identities are not guaranteed to be in the gallery (\textit{open-set}). Compared to a closed-set identification, the latter is definitely more challenging in as much as the system needs firstly to reject impostors (\textit{i.e.}, probe identities missing from the gallery), and subsequently, if the probe is accepted as genuine, retrieve the correct identity. In our case, the probe set is composed of full-length videos from the original dataset, while the gallery is composed of templates, \textit{i.e.}, sets of still images. To collect the images, an automatic application was developed. The main motivations behind this work can be found in both the lack of open-set identification protocols defined in the literature and the undeniable complexity of such. We also argued that extending an existing and widely used dataset could make its distribution easier and that data heterogeneity would make the problem even more challenging and realistic. We named the dataset Extended YTF (E-YTF). Finally, we report baseline recognition results using two well known DCNN architectures.