  The boosting on the need of security notably increased the amount of possible
facial recognition applications, especially due to the success of the Internet
of Things (IoT) paradigm. However, although handcrafted and deep
learning-inspired facial features reached a significant level of compactness
and expressive power, the facial recognition performance still suffers from
intra-class variations such as ageing, facial expressions, lighting changes,
and pose. These variations cannot be captured in a single acquisition and
require multiple acquisitions of long duration, which are expensive and need a
high level of collaboration from the users. Among others, self-update
algorithms have been proposed in order to mitigate these problems.
Self-updating aims to add novel templates to the users' gallery among the
inputs submitted during system operations. Consequently, computational
complexity and storage space tend to be among the critical requirements of
these algorithms. The present paper deals with the above problems by a novel
template-based self-update algorithm, able to keep over time the expressive
power of a limited set of templates stored in the system database. The
rationale behind the proposed approach is in the working hypothesis that a
dominating mode characterises the features' distribution given the client.
Therefore, the key point is to select the best templates around that mode. We
propose two methods, which are tested on systems based on handcrafted features
and deep-learning-inspired autoencoders at the state-of-the-art. Three
benchmark data sets are used. Experimental results confirm that, by effective
and compact feature sets which can support our working hypothesis, the proposed
classification-selection approaches overcome the problem of manual updating
and, in case, stringent computational requirements.
