This work presents a feasibility study of remote attention level estimation
based on eye blink frequency. We first propose an eye blink detection system
based on Convolutional Neural Networks (CNNs), very competitive with respect to
related works. Using this detector, we experimentally evaluate the relationship
between the eye blink rate and the attention level of students captured during
online sessions. The experimental framework is carried out using a public
multimodal database for eye blink detection and attention level estimation
called mEBAL, which comprises data from 38 students and multiples acquisition
sensors, in particular, i) an electroencephalogram (EEG) band which provides
the time signals coming from the student's cognitive information, and ii) RGB
and NIR cameras to capture the students face gestures. The results achieved
suggest an inverse correlation between the eye blink frequency and the
attention level. This relation is used in our proposed method called ALEBk for
estimating the attention level as the inverse of the eye blink frequency. Our
results open a new research line to introduce this technology for attention
level estimation on future e-learning platforms, among other applications of
this kind of behavioral biometrics based on face analysis.

本文提出一种基于眨眼频率的远程注意水平估计方案，通过使用卷积神经网络构建的眨眼检测系统，并分析在线课堂学生的注意力与眨眼率之间的关系，提出一种名为 ALEBk 的方法来估计注意力。研究结果显示出眨眼频率与注意水平之间呈现反相关的关系，为在未来的远程学习平台上引入这项技术及基于面部分析的这类行为生物识别技术应用打开了新的研究方向。