  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.
