Abstract
We present a novel online unsupervised method for face
identity learning from video streams. The method exploits
deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative descriptor matching solution based on Reverse Nearest
Neighbour and a forgetting strategy that detect redundant
descriptors and discard them appropriately while time progresses. It is shown that the proposed learning procedure
is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results
show that the proposed method achieves comparable results
in the task of multiple face tracking and better performance
in face identification with offline approaches exploiting future information. Code will be publicly available.