Multimodal recognition of frustration during game-play with deep neural networks

Abstract

Frustration, which is one aspect of the field of emotional recognition, is of particular interest to the video game industry as it provides information concerning each individual player’s level of engagement. The use of non-invasive strategies to estimate this emotion is, therefore, a relevant line of research with a direct application to real-world scenarios. While several proposals regarding the performance of non-invasive frustration recognition can be found in literature, they usually rely on hand-crafted features and rarely exploit the potential inherent to the combination of different sources of information. This work, therefore, presents a new approach that automatically extracts meaningful descriptors from individual audio and video sources of information using Deep Neural Networks (DNN) in order to then combine them, with the objective of detecting frustration in Game-Play scenarios. More precisely, two fusion modalities, namely decision-level and feature-level, are presented and compared with state-of-the-art methods, along with different DNN architectures optimized for each type of data. Experiments performed with a real-world audiovisual benchmarking corpus revealed that the multimodal proposals introduced herein are more suitable than those of a unimodal nature, and that their performance also surpasses that of other state-of-the–art approaches, with error rate improvements of between 40% and 90%.