  The ability to recognize human partners is an important social skill to build
personalized and long-term human-robot interactions, especially in scenarios
like education, care-giving, and rehabilitation. Faces and voices constitute
two important sources of information to enable artificial systems to reliably
recognize individuals. Deep learning networks have achieved state-of-the-art
results and demonstrated to be suitable tools to address such a task. However,
when those networks are applied to different and unprecedented scenarios not
included in the training set, they can suffer a drop in performance. For
example, with robotic platforms in ever-changing and realistic environments,
where always new sensory evidence is acquired, the performance of those models
degrades. One solution is to make robots learn from their first-hand sensory
data with self-supervision. This allows coping with the inherent variability of
the data gathered in realistic and interactive contexts. To this aim, we
propose a cognitive architecture integrating low-level perceptual processes
with a spatial working memory mechanism. The architecture autonomously
organizes the robot's sensory experience into a structured dataset suitable for
human recognition. Our results demonstrate the effectiveness of our
architecture and show that it is a promising solution in the quest of making
robots more autonomous in their learning process.
