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This work introduces a new multispectral database and novel approaches for
eyeblink detection in RGB and Near-Infrared (NIR) individual images. Our
contributed dataset (mEBAL2, multimodal Eye Blink and Attention Level
estimation, Version 2) is the largest existing eyeblink database, representing
a great opportunity to improve data-driven multispectral approaches for blink
detection and related applications (e.g., attention level estimation and
presentation attack detection in face biometrics). mEBAL2 includes 21,100 image
sequences from 180 different students (more than 2 million labeled images in
total) while conducting a number of e-learning tasks of varying difficulty or
taking a real course on HTML initiation through the edX MOOC platform. mEBAL2
uses multiple sensors, including two Near-Infrared (NIR) and one RGB camera to
capture facial gestures during the execution of the tasks, as well as an
Electroencephalogram (EEG) band to get the cognitive activity of the user and
blinking events. Furthermore, this work proposes a Convolutional Neural Network
architecture as benchmark for blink detection on mEBAL2 with performances up to
97%. Different training methodologies are implemented using the RGB spectrum,
NIR spectrum, and the combination of both to enhance the performance on
existing eyeblink detectors. We demonstrate that combining NIR and RGB images
during training improves the performance of RGB eyeblink detectors (i.e.,
detection based only on a RGB image). Finally, the generalization capacity of
the proposed eyeblink detectors is validated in wilder and more challenging
environments like the HUST-LEBW dataset to show the usefulness of mEBAL2 to
train a new generation of data-driven approaches for eyeblink detection.