Adjusting the Difficulty of Running Game with Facial Expression Recognition Technology Using Convolutional Neural Network
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초록
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.
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.
참고문헌 (9)
- Black, M. J., "Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion", Computer Vision, IEEE, pp.374~381 (1995)
- Chen Xiangzhen, "Research of Facial Expression Recognition Algorithm Based on Deep Learning", pp.11~16 (2016)
- B.FASEL, "Automatic Facial Expression Analysis: A Survey", Pattern Recognition, 36(1), pp.259~275 (2003)[DOI]
- Yi Sun, "DeepID3: Face Recognition with Very Deep Neural Networks", Computer Vision and Pattern Recognition (2015)
- Yann LeCun, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86(11), pp.2278~2324 (1998)[DOI]
- 李瑞森, "游戏专业概论", pp.2~17 (2015), 清华大学出版社
- Su Xue, "Dynamic Difficulty Adjustment for Maximized Engagement in Digital Games", International Conference, pp.465~471 (2017)