@inbook{39f80da22836426695656911704c8c9d,
title = "Biometric tools for learner identity in e-assessment",
abstract = "Biometric tools try model a person by means of its intrinsic properties or behaviours. Every person has a set of unique physical traits derived from genetics and vital experience. Although there are many traits that can be used to verify the identity of a learner, such as the voice, appearance, fingerprints, iris, or gait among others, most of them require the use of special sensors. This chapter presents an analysis of four biometric tools based on standard sensors and used during the TeSLA project pilots. Those tools are designed to verify the identity of the learner during an assessment activity. The data for on-site and on-line institutions is used in order to compare the performance of such tools in both scenarios.",
keywords = "Biometrics, Face recognition, Forensic analysis, Identity verification, Keystroke dynamics recognition, Learner identification, Voice recognition",
author = "Xavier Bar{\'o} and \{Mu{\~n}oz Bernaus\}, Roger and David Baneres and Guerrero-Rold{\'a}n, \{Ana Elena\}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.",
year = "2020",
doi = "10.1007/978-3-030-29326-0\_3",
language = "English",
isbn = "9783030293253",
series = "Lecture Notes on Data Engineering and Communications Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "41--65",
editor = "David Baneres and Rodr{\'i}guez, \{M. Elena\} and \{Guerrero Rold{\'a}n\}, \{Ana Elena\}",
booktitle = "Engineering Data-Driven Adaptive Trust-based e-Assessment Systems",
}