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.

翻译：本文介绍了一种新的多光谱数据库及针对RGB和近红外（NIR）单帧图像的眨眼检测创新方法。我们贡献的数据集（mEBAL2，多模态眨眼与注意力水平估计第二版）是现有最大的眨眼数据库，为提升基于数据驱动的多光谱眨眼检测及相关应用（如面部生物特征中的注意力水平评估和呈现攻击检测）提供了重要机遇。mEBAL2包含来自180名不同学生的21,100个图像序列（总计超过200万张标注图像），这些学生在完成难度不等的在线学习任务或通过edX慕课平台学习HTML初级课程时被采集。该数据库采用多传感器系统，包括两个近红外（NIR）传感器和一个RGB摄像头以捕捉任务执行时的面部动态，同时使用脑电图（EEG）头带获取用户认知活动与眨眼事件。此外，本文提出了一种卷积神经网络架构作为mEBAL2的眨眼检测基准，检测性能高达97%。通过使用RGB光谱、NIR光谱及其融合训练方法，我们实现了对现有眨眼检测器性能的增强。实验证明，在训练过程中结合NIR与RGB图像可提升仅基于RGB图像的眨眼检测器性能。最终，我们在更具挑战性的HUST-LEBW数据集上验证了所提眨眼检测器的泛化能力，展现了mEBAL2在训练新一代数据驱动眨眼检测方法中的实用价值。