People’s facial expressions, whether made consciously or subconsciously, continuously revealtheir state of mind. This work proposes a methodfor predicting people’s strategic decisions based ontheir facial expressions. We designed a new version of the centipede game that intorduces an incentive for the human participant to hide her facial expressions. We recorded on video participants whoplayed several games of our centipede version, andconcurrently logged their decisions throughout thegames. The video snippet of the participants’ facesprior to their decisions is represented as a fixed-size vector by estimating the covariance matrix of keyfacial points which change over time. This vectorserves as input to a classifier that is trained to predict the participant’s decision. We compare severaltraining techniques, all of which are designed towork with the imbalanced decisions typically madeby the players of the game. Furthermore, we investigate adaptation of the trained model to eachplayer individually, while taking into account theplayer’s facial expressions in the previous games.The results show that our method outperforms standard SVM as well as humans in predicting subjects’strategic decisions. To the best of our knowledge,this is the first study to present a methodology forpredicting people’s strategic decisions when thereis an incentive to hide facial expressions.