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

arXiv: https://arxiv.org/abs/2006.05327 · HTML: https://mlsanity.com/paper/2006.05327/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 19 of 19)

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- [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)
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- [Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video](https://mlsanity.com/paper/2303.16053/) (2023)
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- [A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data](https://mlsanity.com/paper/2601.07576/) (2026)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [Spatiotemporal Pyramidal CNN with Depth-Wise Separable Convolution for Eye Blinking Detection in the Wild](https://mlsanity.com/paper/2306.11287/) (2023)
- [Deep Learning Technology for Face Forgery Detection: A Survey](https://mlsanity.com/paper/2409.14289/) (2024)

## Abstract (arXiv)

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 detectors of eye blinks have been proposed for many tasks including attention level estimation, analysis of neuro-degenerative diseases, deception recognition, drive fatigue detection, or face anti-spoofing. However, most existing databases and algorithms in this area are limited to experiments involving only a few hundred samples and individual sensors like face cameras. The proposed mEBAL improves previous databases in terms of acquisition sensors and samples. In particular, three different sensors are simultaneously considered: Near Infrared (NIR) and RGB cameras to capture the face gestures and an Electroencephalography (EEG) band to capture the cognitive activity of the user and blinking events. Regarding the size of mEBAL, it comprises 6,000 samples and the corresponding attention level from 38 different students while conducting a number of e-learning tasks of varying difficulty. In addition to presenting mEBAL, we also include preliminary experiments on: i) eye blink detection using Convolutional Neural Networks (CNN) with the facial images, and ii) attention level estimation of the students based on their eye blink frequency.