  In the last five years, deep learning methods, in particular CNN, have
attracted considerable attention in the field of face-based recognition,
achieving impressive results. Despite this progress, it is not yet clear
precisely to what extent deep features are able to follow all the intra-class
variations that the face can present over time. In this paper we investigate
the performance the performance improvement of face recognition systems by
adopting self updating strategies of the face templates. For that purpose, we
evaluate the performance of a well-known deep-learning face representation,
namely, FaceNet, on a dataset that we generated explicitly conceived to embed
intra-class variations of users on a large time span of captures: the
APhotoEveryday (APE) dataset. Moreover, we compare these deep features with
handcrafted features extracted using the BSIF algorithm. In both cases, we
evaluate various template update strategies, in order to detect the most useful
for such kind of features. Experimental results show the effectiveness of
"optimized" self-update methods with respect to systems without update or
random selection of templates.
