BiBECA: Biometrics and Behavior for Context-Aware and Secure Human-Computer Interaction (2019-2021)
Founded by: MINECO/MICINN (RTI2018-101248-B-I00 MINECO)
The project BIBECA aims at breaking the limits of current biometric technology typically applied only for person recognition to enable a plethora of new applications in Human-Computer Interaction systems, Recreation, Health, and in general technology for Well Being. This is motivated by the demographic and social changes, with an aging population with ever-growing attendance needs, and always-connected new generations with powerful smartphones and wearable devices.
BIBECA will develop a series of enabling-technologies for processing, analyzing, and exploiting a series of largely unexplored biometric signals and related metadata captured in daily life scenarios using smartphones and tablets, wearable devices, common desktop and laptop computing interfaces, and new sensors to arrive in digital home assistants. Those signals include: touchscreen gestures, motion information, context metadata, face video, physiological signals, and mouse and keystroke dynamics; using as auxiliary tools also speech and scene understanding technologies, to which the new developments will be integrated in continuous time-adaptive schemes.
BIBECA will then advance three questions using those new technologies: WHICH sensor-dependent and sensor-independent features in the interaction of the user with the technology work best for person recognition and stress/emotion monitoring, WHEN, i.e., under which circumstances (e.g., user habituation, signal quality, context, user-dependent behavior anomalies, etc.), and HOW can we best exploit jointly those signals and context information for various applications with emphasis on e-Security, e-Health, and improved HCI.
Javier Hernandez-Ortega, Roberto Daza, Aythami Morales, Julian Fierrez and Ruben Tolosana, “Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB,” Proc. of IEEE In.t Conf. on Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, July 2020.
A. Acien, A. Morales, J. Fierrez, R. Vera Rodriguez and J. Hernandez-Ortega, “Active Detection of Age Groups Based on Touch Interaction“, IET Biometrics, vol. 8, n. 1, pp. 101-108, January 2019.
TRESPASS: Training in Secure and Privacy-preserving Biometrics (2020-2024)
Founded by: EU-H2020 (MSCA-ITN-2019-860813)
To combat rising security challenges, the global market for biometric technologies is growing at a fast pace. It includes all processes used to recognise, authenticate and identify persons based on biological and/or behavioural characteristics.
The EU-funded TReSPAsS-ETN project will deliver a new type of security protection (through generalised presentation attack detection (PAD) technologies) and privacy preservation (through computationally feasible encryption solutions).
The TReSPAsS-ETN Marie Skłodowska-Curie early training network will couple specific technical and transferable skills training including entrepreneurship, innovation, creativity, management and communications with secondments to industry.
A. Morales, A. Acien, J. Fierrez, J.V. Monaco, R. Tolosana, R. Vera-Rodriguez, Javier Ortega-Garcia, “Keystroke Biometrics in Response to Fake News Propagation in a Global Pandemic,” Proc. of IEEE International Workshop on Secure Digital Identity Management (SDIM), Madrid, Spain, 2020. [pdf][media]
A. Acien, A. Morales, J. Fierrez, R. Vera-Rodriguez, O. Delgado-Mohatar, “BeCAPTCHA: Bot Detection in Smartphone Interaction using Touchscreen Biometrics and Mobile Sensors“, arXiv:2005.13655, 2020. [pdf]