EmoGame: Towards a self-rewarding methodology for capturing children faces in an engaging context

Abstract

Facial expression datasets are currently limited as most of them only capture the emotional expressions of adults. Researchers have begun to assert the importance of having child exemplars of the various emotional expressions in order to study the interpretation of these expressions developmentally. Capturing children expression is more complicated as the protocols used for eliciting and recording expressions for adults are not necessarily adequate for children. This paper describes the creation of a flexible Emotional Game for capturing children faces in an engaging context. The game is inspired by the well-known Guitar HeroTM gameplay, but instead of playing notes, the player should produce series of expressions. In the current work, we measure the capacity of the game to engage the children and we discuss the requirements in terms of expression recognition needed to ensure a viable gameplay. The preliminary experiments conducted with a group of 12 children with ages between 7 and 11 in various settings and social contexts show high levels of engagement and positive feedback.