CNN을 이용한 표정인식 기술에 기반한 러닝게임의 난이도 조절

Abstract

Many games nowadays have a certain share of the market. However, to maintain the market position for long is not common. The most appealing element for gamers is Game Fun. The element that can make game interesting is the game difficulty. While the game difficulty has no uniform evaluation standard until now. The proposed paper uses a continuous convolutional neural network with SVM classifier to recognize the player's expression in real time. The system infers the player's psychological activity based on different expressions and makes adjustments to the game difficulty level to meet the user's needs. And the experiment result shows that the facial expression recognition system using deep learning could increase the play-time and score of the game, and promote the fun of games.