BiBECA: Biometrics and Behavior for Context-Aware and Secure Human-Computer Interaction (2019-2021)
Title: Biometrics and Behavior for Context-Aware and Secure Human-Computer Interaction
Type: Spanish National R&D Program
Funding: ca. 243 Keuros
Participants: Univ. Autonoma de Madrid
Period: January 2019 – December 2021
Principal investigator(s): Julian Fierrez and Aythami Morales
- Generating a better understanding about the nature of biometrics in terms of distinctiveness, permanence, and relation to stress levels and emotions. New methods for analyzing and modelling complex yet structured relations on heterogeneous data will be developed in BIBECA.
- Design of robust algorithms in terms of biometric representation and matching from uncooperative users in unconstrained and varying scenarios. BIBECA will contribute in this topic to: innovative adaptive fusion schemes, and public databases with heterogeneous continuous biometrics, in realistic mobile and desktop setups.
- Experimental exploration and technology development towards new biometrics applications: mobile authentication and ubiquitous biometrics.
- Understand and improve the usability of biometric systems.
- Understand and improve the security in biometric systems. BIBECA will track the advances in security against attacks and privacy preservation in biometric systems, and will adapt and investigate those advances for the kind of human interaction signals to be explored during the project implementation.
A. Morales, J. Fierrez, R. Vera-Rodriguez, R. Tolosana, “SensitiveNets: Learning Agnostic Representations with Application to Face Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43 (6), pp. 2158-2164, 2021.
A. Morales, J. Fierrez, A. Acien, R. Tolosana, I. Serna, “SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation,” Pattern Recognition, vol. 122 (108283), 2022.
A. Ortega, J. Fierrez, A. Morales, Z. Wang, M. Cruz, C. Alonso, T. Ribeiro,”Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Explaining Biases in Machine Learning“, Computers, 10 (11), pp. 154, 2021.
A. Acien, A. Morales, J.V. Monaco, R. Vera-Rodriguez, J. Fierrez, “TypeNet: Deep Learning Keystroke Biometrics,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 4 (1), pp. 57-70, 2022.
NEUROMETRICS: New Biomarkers to Characterize Neurodegenerative Diseases (2017-2018)
Founded by: PROYECTOS DE COOPERACIÓN INTERUNIVERSITARIA UAM-BANCO SANTANDER con América Latina
The human body is constantly communicating information about our health. This information can be captured and processed to model the cognitive and neuromotorhealth of the users. Traditionally, such measurements are taken manually by Physicians during isolated visits. Biometric signal processing involves the analysis of these measurements to provide useful information upon which clinicians can make decisions. This project has explored new ways to process these signals using a variety of sensors and new artificial intelligence algorithms. It is time to redefine some of the traditional biomarkers to exploit such a new technology capabilities.
Handwriting dynamics: The handwriting is a behavioural biometric trait which comprises neuromotor characteristics of the user (e.g. our brain and muscles among other factors define the way we write) as well as socio-cultural influence (e.g. the Western and Asian styles). The dynamics of handwriting include rich patterns related with velocity, acceleration or angular information, among others.
Mouse dynamics: Mouse dynamics are derived from the user-mouse interaction. The mouse trajectories include information related with neuromotor capabilities of the user that can be derived from velocity profiles and precision. There is a large room for research focus on the development of specific task to reveal the user state.
Touch dynamics: The great popularity of smartphones/tablets and the increase in their use in everyday applications has led to develop new applications based on touch interactions with the screens. Keystroking, handwriting or mouse have been replaced by simple touch actions in our device interaction.
L. F. Gomez, A. Morales, J. R. Orozco-Arroyave, R. Daza, J. Fierrez, “Improving Parkinson Detection Using Dynamic Features From Evoked Expressions in Video,” IEEE/CVF Conference on Computer Vision and Pattern Recognition – First International Workshop on Affective Understanding in Video (CVPR-AUVi), pp. 1562-1570, 2021. [pdf]
Luis F. Gomez-Gomez, A. Morales, J. Fierrez, J. R. Orozco-Arroyave, “Exploring Facial Expressions and Affective Domains for Parkinson Detection,” arXiv:2012.06563, 2020. [pdf]
R. Castrillon, A. Acien, J. Orozco-Arroyave, A. Morales, J. Vargas, R.Vera-Rodrıguez, J. Fierrez, J. Ortega-Garcia and A. Villegas, “Characterization of the Handwriting Skills as a Biomarker for Parkinson Disease“, Proc. of IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) – Human Health Monitoring Based on Computer Vision, Lille, France, April 2019. [pdf]