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.

这篇论文介绍了一个名为mEBAL的多模态数据库，用于眨眼检测和注意力水平估计，其改进了现有数据库在采集传感器和样本方面的限制，并基于学生眨眼频率估计其注意力水平，初步实验表明使用卷积神经网络在面部图像中眨眼检测的效果比较好。

mEBAL:用于眨眼检测和注意力估计的多模态数据库