Dynamic Difficulty Adjustment by Facial Expression

To enhance players’ game experience is one of the most important targets in game exploring. In this paper we integrated a dynamic game difficulty adjustment (DDA) method into Tetris that using Active Shape Model (ASM) and HMM to recognize emotion states of players’ from camera and utilizing Kalman filter to dynamically detect the experience of players. According to the users’ experience then adjusted the speed of game. Experiments shown that our DDA method could give player better game experience.

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