The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a machine is referred to as `user engagement'. Engagement comprises of behavioral, cognitive, and affect related cues. To create engagement prediction systems that can work in real-world conditions, it is quintessential to learn from rich, diverse datasets. To this end, a large scale multi-faceted engagement in the wild dataset EngageNet is proposed. 31 hours duration data of 127 participants representing different illumination conditions are recorded. Thorough experiments are performed exploring the applicability of different features, action units, eye gaze, head pose, and MARLIN. Data from user interactions (question-answer) are analyzed to understand the relationship between effective learning and user engagement. To further validate the rich nature of the dataset, evaluation is also performed on the EngageWild dataset. The experiments show the usefulness of the proposed dataset. The code, models, and dataset link are publicly available at https://github.com/engagenet/engagenet_baselines.

翻译：个体在与机器交互时所表现出的专注、热情、乐观和投入程度被称为“用户参与度”。参与度包含行为、认知和情感相关的线索。为了构建能够在真实环境中工作的参与度预测系统，从丰富多样的数据集中学习至关重要。为此，本文提出了一个大规模、多方面的自然场景参与度数据集EngageNet。记录了127名参与者在不同照明条件下的31小时时长数据。进行了详尽的实验，探索了不同特征、动作单元、视线方向、头部姿态以及MARLIN的适用性。分析了用户交互（问答环节）的数据，以理解有效学习与用户参与度之间的关系。为进一步验证数据集丰富性，还在EngageWild数据集上进行了评估。实验证明了所提出数据集的有效性。代码、模型和数据集链接已在https://github.com/engagenet/engagenet_baselines公开提供。