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

arXiv: https://arxiv.org/abs/2112.09165 · HTML: https://mlsanity.com/paper/2112.09165/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors](https://mlsanity.com/paper/1807.00966/) (2018)
- [DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations](https://mlsanity.com/paper/2009.06308/) (2020)
- [mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation](https://mlsanity.com/paper/2006.05327/) (2020)
- [edBB: Biometrics and Behavior for Assessing Remote Education](https://mlsanity.com/paper/1912.04786/) (2019)
- [Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication](https://mlsanity.com/paper/2006.00790/) (2020)
- [BeCAPTCHA-Mouse: Synthetic Mouse Trajectories and Improved Bot Detection](https://mlsanity.com/paper/2005.00890/) (2020)
- [Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices](https://mlsanity.com/paper/1902.07891/) (2019)
- [A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos](https://mlsanity.com/paper/2005.11101/) (2020)

## Built on by (most cited first, 13 of 13)

- [edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms](https://mlsanity.com/paper/2211.09210/) (2022)
- [M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Education](https://mlsanity.com/paper/2305.12561/) (2023)
- [VAAD: Visual Attention Analysis Dashboard applied to e-Learning](https://mlsanity.com/paper/2405.20091/) (2024)
- [A multimodal dataset for understanding the impact of mobile phones on remote online virtual education](https://mlsanity.com/paper/2412.14195/) (2024)
- [Biometrics and Behavior Analysis for Detecting Distractions in e-Learning](https://mlsanity.com/paper/2405.15434/) (2024)
- [mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink Detection](https://mlsanity.com/paper/2309.07880/) (2023)
- [MATT: Multimodal Attention Level Estimation for e-learning Platforms](https://mlsanity.com/paper/2301.09174/) (2023)
- [DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning](https://mlsanity.com/paper/2408.05523/) (2024)
- [SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality](https://mlsanity.com/paper/2501.10977/) (2025)
- [AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning](https://mlsanity.com/paper/2506.17364/) (2025)
- [Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video](https://mlsanity.com/paper/2303.16053/) (2023)
- [M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards](https://mlsanity.com/paper/2502.15363/) (2025)
- [A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data](https://mlsanity.com/paper/2601.07576/) (2026)

## Abstract (arXiv)

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 Neural Networks (CNNs), very competitive with respect to related works. Using this detector, we experimentally evaluate the relationship between the eye blink rate and the attention level of students captured during online sessions. The experimental framework is carried out using a public multimodal database for eye blink detection and attention level estimation called mEBAL, which comprises data from 38 students and multiples acquisition sensors, in particular, i) an electroencephalogram (EEG) band which provides the time signals coming from the student's cognitive information, and ii) RGB and NIR cameras to capture the students face gestures. The results achieved suggest an inverse correlation between the eye blink frequency and the attention level. This relation is used in our proposed method called ALEBk for estimating the attention level as the inverse of the eye blink frequency. Our results open a new research line to introduce this technology for attention level estimation on future e-learning platforms, among other applications of this kind of behavioral biometrics based on face analysis.