Learning representations of players' emotions and state for next generation gaming

计算机和控制台游戏平台越来越多地配备有传感器，例如相机和深度传感器，这些传感器可以在玩家玩游戏时观察和跟踪玩家。然而，在真实的时间内分析这些数据，而不仅仅是身体跟踪的部分，以可靠地识别和跟踪用户在游戏过程中的精确情绪状态，仍然是一个科学和技术挑战。我们与总部位于美国的合作伙伴Ubisoft一起，建议利用机器学习技术的最新进展来实现这一目标。具体来说，我们将采用最近的方法在大数据集上训练深度神经网络，以学习玩家捕获的图像（特别是面部）和情绪状态之间的映射。这项研究将专注于改进这些技术，以快速、透明地适应每个玩家的特定面部表情模式。加拿大的电脑游戏产业将获得一个主要的竞争优势，由于这种技术，因为游戏系统的能力，准确地感知玩家的情绪状态在真实的时间将使游戏适应它，并预计将成为关键的制作下一代真正身临其境的游戏体验。

Computer and console gaming platforms are increasingly equiped with sensors, such as cameras and depth sensors, that can watch and track players while they are playing a game. It however remains a scientific and technical challenge to analyse this data in real time, beyond mere parts of body tracking, to reliably recognize and track a user's precise emotional state during gameplay. With our Montreal-based partner Ubisoft, we propose to leverage recent advances in machine learning techniques to achieve this. Specifically we will employ recent approaches for training deep neural networks on big datasets, to learn a mapping between a player's captured images (especially the face) and emotional state. The research will focus on improving these techniques to allow fast and transparent adaptation to each player's specific modes of facial expression. The Canadian computer gaming industry would gain a major competitive advantage thanks to such technology, since the ability of a gaming system to accurately perceive a player's emotional state in real time will enable the game to adapt to it, and is anticipated to be the key for crafting the next generation of truly immersive gaming experience.