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IEEE Transactions on Affective Computing
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Facial expression analysis systems often employ machine learning algorithms that depend a lot on the quality of the face database they are trained on. Unfortunately, generating high quality face databases is a major challenge that is rather time consuming. We have developed BeFaced, a tile-matching casual tablet game to enable massive crowdsourcing of facial expressions for the purpose of such machine learning algorithms. Based on the popular tile-matching gameplay mechanic, players are required to make facial expressions shown on matched tiles in order to clear them and advance in the game. Dynamic difficulty adjustment of the recognition accuracy is employed in the game in order to increase engagement and hence increase the quantity of varied facial expressions obtained. Each facial expression is automatically captured, labelled and sent to our online face database. At a more abstract level, BeFaced investigates a novel method of using popular game mechanics to aid the advancement of computer vision algorithms.