  We present a novel unsupervised method for face identity learning from video
sequences. The method exploits the ResNet deep network for face detection and
VGGface fc7 face descriptors together with a smart learning mechanism that
exploits the temporal coherence of visual data in video streams. We present a
novel feature matching solution based on Reverse Nearest Neighbour and a
feature forgetting strategy that supports incremental learning with memory size
control, while time progresses. It is shown that the proposed learning
procedure is asymptotically stable and can be effectively applied to relevant
applications like multiple face tracking.
