  Deep Learning approaches have brought solutions, with impressive performance,
to general classification problems where wealthy of annotated data are provided
for training. In contrast, less progress has been made in continual learning of
a set of non-stationary classes, mainly when applied to unsupervised problems
with streaming data.
  Here, we propose a novel incremental learning approach which combines a deep
features encoder with an Open-Set Dynamic Ensembles of SVM, to tackle the
problem of identifying individuals of interest (IoI) from streaming face data.
From a simple weak classifier trained on a few video-frames, our method can use
unsupervised operational data to enhance recognition. Our approach adapts to
new patterns avoiding catastrophic forgetting and partially heals itself from
miss-adaptation. Besides, to better comply with real world conditions, the
system was designed to operate in an open-set setting. Results show a benefit
of up to 15% F1-score increase respect to non-adaptive state-of-the-art
methods.
