| dc.description.abstract | Biometric technologies have historically been explored as Pattern
Recognition systems. Over the past three decades, biometric-
based human recognition systems have significantly changed
and improved. Applications in forensics, surveillance, healthcare,
automotive, and human-computer interaction have benefited of
such advancements and are currently globally used. However,
the exclusive focus on pattern recognition may obscure or re-
strict the potential and capabilities of this discipline. Emerging
biometric modalities have begun to impact the security of sensi-
tive data, information, and systems. As the biometric challenges
increase, the solution strategies shifted the attention on human
behavior. Behavioral biometrics is the study of patterns in human
activities that can be uniquely identified and measured. Recent
technological advancements, especially in Artificial Intelligence,
as well as hardware development, have increased the potential
of biometric approaches and expanded their application fields.
Beginning with the wide concept of behavioral biometrics, this
thesis aims to advance the state-of-the-art in several applications,
such as estimating an individual’s head rotation to determine
its intent or attention and analyzing facial expressions to detect
human emotional state. Finally, behavioral biometric traits are in-
vestigated through users’ touch interactions with modern mobile
devices. For each of the presented methods, the complete pro-
cessing pipeline is described, including data acquisition, feature
extraction, experimental protocols, and decision-making, as well
as a comparison to state-of-the-art methods to show advantages
and discuss current challenges. [edited by Author] | it_IT |
