Audiovisual Analysis for Recognising Frustration during Game-Play: Introducing the Multimodal Game Frustration Database

Automatic recognition of frustration, by analysing facial and vocal expressions, can help user experience designers to identify interaction obstacles. To encourage the development of automated systems such as these, we present a novel audiovisual database: the Multimodal Game Frustration Database (MGFD), consisting of ca. 5 hours of audiovisual data, collected from 67 Chinese students speaking in English. For data collection, we developed ‘Crazy Trophy’, a Wizard-of-Oz voice activated web-game designed with a variety of usability problems and aimed to induce increasing amounts of frustration. We also present a baseline for binary multimodal frustration classification (frustration vs no-frustration). For this, we compare the performance of a conventional method, Support Vector Machine classifier, and a state-of-the-art method utilising Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), extracting both audio (Mel-frequency Cepstral Coefficients) and video (facial action units) features. Using LSTM-RNN and a feature-based multi-model fusion strategy, the best result acheived for the baseline was 60.3 % UAR. To enable further research in this area, the game (‘Crazy Trophy’), the database (MGFD), and the partitioning considered in the presented baseline, are made accessible to the research community.