Towards an "In-the-Wild" Emotion Dataset Using a Game-based Framework
This addresses the need for better emotion datasets for researchers, but it is incremental as it builds on existing methods with a new collection approach.
The paper tackled the problem of creating a large, balanced 'in-the-wild' facial emotion dataset by proposing a game-based framework, resulting in the collection of over 15,000 images in a month and showing that models trained on this dataset outperform those on CIFE for robust emotion detection.
In order to create an "in-the-wild" dataset of facial emotions with large number of balanced samples, this paper proposes a game-based data collection framework. The framework mainly include three components: a game engine, a game interface, and a data collection and evaluation module. We use a deep learning approach to build an emotion classifier as the game engine. Then a emotion web game to allow gamers to enjoy the games, while the data collection module obtains automatically-labelled emotion images. Using our game, we have collected more than 15,000 images within a month of the test run and built an emotion dataset "GaMo". To evaluate the dataset, we compared the performance of two deep learning models trained on both GaMo and CIFE. The results of our experiments show that because of being large and balanced, GaMo can be used to build a more robust emotion detector than the emotion detector trained on CIFE, which was used in the game engine to collect the face images.