An experimental investigation on self adaptive facial recognition algorithms using a long time span data set

Nowadays, facial authentication systems are present in many daily life devices. Their performance is influenced by the appearance of the facial trait that changes according to many factors such as lighting, pose, variations over time and obstructions. Adaptive systems follow these variations by updating themselves through images acquired during system operations. Although the literature proposes many possible approaches, their evaluation is often left to data set not explicitly conceived to simulate a real application scenario. The substantial absence of an appropriate and objective evaluation set is probably the motivation of the lack of implementation of adaptive systems in real devices. This paper presents a facial dataset acquired by videos in the YouTube platform. The collected images are particularly suitable for evaluating adaptive systems as they contain many changes during the time-sequence. A set of experiments of the most representative self adaptive approaches recently appeared in the literature is also performed and discussed. They allow to give some initial insights about pros and cons of facial adaptive authentication systems by considering a medium-long term time window of the investigated systems performance