Automatic engagement recognition is a technique that is used to measure the engagement level of people in a specific task. Although previous research has utilized expensive and intrusive devices such as physiological sensors and pressure-sensing chairs, methods using RGB video cameras have become the most common because of the cost efficiency and noninvasiveness of video cameras. Automatic engagement recognition methods using video cameras are usually based on hand-crafted features and a statistical temporal dynamics modeling algorithm. This paper proposes a data-driven convolutional neural networks (CNNs)-based engagement recognition method that uses only facial images from input videos. As the amount of data in a dataset of children's engagement is insufficient for deep learning, pre-trained CNNs are utilized for low-level feature extraction from each video frame. In particular, a new layer combination for temporal dynamics modeling is employed to extract high-level features from low-level features. Experimental results on a database created using images of children from kindergarten demonstrate that the performance of the proposed method is superior to that of previous methods. The results indicate that the engagement level of children can be gauged automatically via deep learning even when the available database is deficient.

中文翻译：

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使用卷积神经网络从面部视频中自动识别儿童参与度

自动参与度识别是一种用于衡量人们在特定任务中的参与度的技术。尽管之前的研究使用了昂贵且侵入性的设备，例如生理传感器和压力感应椅，但由于摄像机的成本效益和非侵入性，使用 RGB 摄像机的方法已成为最常见的方法。使用摄像机的自动参与识别方法通常基于手工制作的特征和统计时间动态建模算法。本文提出了一种基于数据驱动的卷积神经网络 (CNN) 的参与识别方法，该方法仅使用来自输入视频的面部图像。由于儿童参与度数据集中的数据量不足以进行深度学习，预训练的 CNN 用于从每个视频帧中提取低级特征。特别是，一种用于时间动态建模的新层组合用于从低级特征中提取高级特征。在使用幼儿园儿童图像创建的数据库上的实验结果表明，所提出的方法的性能优于以前的方法。结果表明，即使可用数据库不足，也可以通过深度学习自动衡量儿童的参与度。在使用幼儿园儿童图像创建的数据库上的实验结果表明，所提出的方法的性能优于以前的方法。结果表明，即使可用数据库不足，也可以通过深度学习自动衡量儿童的参与度。在使用幼儿园儿童图像创建的数据库上的实验结果表明，所提出的方法的性能优于以前的方法。结果表明，即使可用数据库不足，也可以通过深度学习自动衡量儿童的参与度。