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

TL;DR

This paper introduces mEBAL, a comprehensive multimodal database with 6,000 samples from 38 students, combining face cameras and EEG to improve eye blink detection and attention level estimation in e-learning contexts.

Contribution

The paper presents mEBAL, a novel multimodal database that enhances previous datasets by including multiple sensors and a larger sample size for attention and blink analysis.

Findings

Preliminary CNN-based eye blink detection achieved promising results.

Attention levels correlated with eye blink frequency in e-learning tasks.

mEBAL provides a valuable resource for future research in cognitive and attention analysis.

Abstract

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.