activity
20192026
most citededBB: Biometrics and Behavior for Assessing Remote Education

29 citations · 88 across the 22 of their papers we have counts for

collaborators

24 papers

cs.CV2026

VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running

Luis F. Gomez, Julian Fierrez, Roberto Daza +5

Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources. These developments h…

cs.AI2026

AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanations

Javier Irigoyen, Roberto Daza, Francisco Jurado +5

We present AIriskEval-edu Demo, a platform that audits the pedagogical quality of instructional explanations and provides explainable audit results. The platform evaluates an expla…

cs.CY2026

Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond

Javier Irigoyen, Roberto Daza, Aythami Morales +5

The society and emerging risk-based regulatory frameworks for AI underscore the need for rigorous risk assessment to ensure safe and reliable AI systems. In response to this impera…

cs.CL2026

AIriskEval-edu: New Dataset for Risk Assessment in AI-mediated K-12 Educational Explanations

Javier Irigoyen, Roberto Daza, Francisco Jurado +5

This work introduces AIriskEval-edu-db2, a new dataset designed to train and evaluate auditors based on LLMs for an explainable pedagogical risk assessment in instructional content…

cs.HC2026

Evaluating Social Engineering Risks in AI-based Interaction using Biometrics and a Gaming Setup

Roberto Daza, Javier Irigoyen, Ivan Lopez +5

We introduce AIriskEval-gaming, an open platform and dataset to evaluate social engineering risks in LLM-mediated multimodal interaction through controlled games. It supports human…

cs.CV2026

Leveraging Avatar Fingerprinting: A Multi-Generator Photorealistic Talking-Head Public Database and Benchmark

Laura Pedrouzo-Rodriguez, Luis F. Gomez, Ruben Tolosana +4

Recent advances in photorealistic avatar generation have enabled highly realistic talking-head avatars, raising security concerns regarding identity impersonation in AI-mediated co…

cs.AI2026

EduEVAL-DB: A Role-Based Dataset for Pedagogical Risk Evaluation in Educational Explanations

Javier Irigoyen, Roberto Daza, Aythami Morales +4

This work introduces EduEVAL-DB, a dataset based on teacher roles designed to support the evaluation and training of automatic pedagogical evaluators and AI tutors for instructiona…

cs.HC2026

A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data

Alvaro Becerra, Ruth Cobos, Roberto Daza

Oral presentation skills are a critical component of higher education, yet comprehensive datasets capturing real-world student performance across multiple modalities remain scarce.…

cs.CY2025

AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning

Alvaro Becerra, Roberto Daza, Ruth Cobos +3

This work investigates the use of multimodal biometrics to detect distractions caused by smartphone use during tasks that require sustained attention, with a focus on computer-base…

cs.HC2025

MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback

Alvaro Becerra, Daniel Andres, Pablo Villegas +2

In this article, we present a novel multimodal feedback framework called MOSAIC-F, an acronym for a data-driven Framework that integrates Multimodal Learning Analytics (MMLA), Obse…

cs.HC2025

M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards

Alvaro Becerra, Roberto Daza, Ruth Cobos +2

We present a demonstration of a web-based system called M2LADS ("System for Generating Multimodal Learning Analytics Dashboards"), designed to integrate, synchronize, visualize, an…

cs.HC2025★ 4 cited

SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality

Roberto Daza, Lin Shengkai, Aythami Morales +2

This work introduces SMARTe-VR, a platform for student monitoring in an immersive virtual reality environment designed for online education. SMARTe-VR aims to collect data for adap…

cs.HC2024★ 7 cited

A multimodal dataset for understanding the impact of mobile phones on remote online virtual education

Roberto Daza, Alvaro Becerra, Ruth Cobos +2

This work presents the IMPROVE dataset, a multimodal resource designed to evaluate the effects of mobile phone usage on learners during online education. It includes behavioral, bi…

cs.HC2024★ 15 cited

DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning

Roberto Daza, Luis F. Gomez, Julian Fierrez +3

This work introduces an innovative method for estimating attention levels (cognitive load) using an ensemble of facial analysis techniques applied to webcam videos. Our method is p…

cs.CV2024

VAAD: Visual Attention Analysis Dashboard applied to e-Learning

Miriam Navarro, Álvaro Becerra, Roberto Daza +3

In this paper, we present an approach in the Multimodal Learning Analytics field. Within this approach, we have developed a tool to visualize and analyze eye movement data collecte…

cs.CV2024

Biometrics and Behavior Analysis for Detecting Distractions in e-Learning

Álvaro Becerra, Javier Irigoyen, Roberto Daza +4

In this article, we explore computer vision approaches to detect abnormal head pose during e-learning sessions and we introduce a study on the effects of mobile phone usage during…

cs.CV2023★ 11 cited

mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink Detection

Roberto Daza, Aythami Morales, Julian Fierrez +2

This work introduces a new multispectral database and novel approaches for eyeblink detection in RGB and Near-Infrared (NIR) individual images. Our contributed dataset (mEBAL2, mul…

cs.HC2023★ 2 cited

M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Education

Álvaro Becerra, Roberto Daza, Ruth Cobos +3

In this article, we present a Web-based System called M2LADS, which supports the integration and visualization of multimodal data recorded in learning sessions in a MOOC in the for…

cs.CV2023★ 7 cited

MATT: Multimodal Attention Level Estimation for e-learning Platforms

Roberto Daza, Luis F. Gomez, Aythami Morales +4

This work presents a new multimodal system for remote attention level estimation based on multimodal face analysis. Our multimodal approach uses different parameters and signals ob…

cs.HC2022★ 1 cited

edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms

Roberto Daza, Aythami Morales, Ruben Tolosana +3

We present edBB-Demo, a demonstrator of an AI-powered research platform for student monitoring in remote education. The edBB platform aims to study the challenges associated to use…

cs.CV2021★ 12 cited

ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning

Roberto Daza, Daniel DeAlcala, Aythami Morales +3

This work presents a feasibility study of remote attention level estimation based on eye blink frequency. We first propose an eye blink detection system based on Convolutional Neur…

cs.CV2020

mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation

Roberto Daza, Aythami Morales, Julian Fierrez +1

This work presents mEBAL, a multimodal database for eye blink detection and attention level estimation. The eye blink frequency is related to the cognitive activity and automatic d…

cs.HC2020

Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB

Javier Hernandez-Ortega, Roberto Daza, Aythami Morales +2

In this study we estimate the heart rate from face videos for student assessment. This information could be very valuable to track their status along time and also to estimate othe…

cs.HC2019★ 29 cited

edBB: Biometrics and Behavior for Assessing Remote Education

Javier Hernandez-Ortega, Roberto Daza, Aythami Morales +2

We present a platform for student monitoring in remote education consisting of a collection of sensors and software that capture biometric and behavioral data. We define a collecti…