  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.
