Browse > Article

Study on Using Deep Learning Method to Realize The Emotion Linkage between The Gamer and His Avatar in Poker Game  

Ge, Ji Yong (Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University)
Lee, Hye Moon (Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University)
Lee, Won Hyung (Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University)
Publication Information
Journal of the Korean Society for Computer Game / v.31, no.2, 2018 , pp. 47-55 More about this Journal
Abstract
Compared to other types of games, poker game is a psychological game based on gamer's psychological activity. This paper proposes a method based on convolutional neural network (CNN) and support vector machine (SVM) to realize the emotion recognition to link the gamer and his avatar in online poker game. The CNN model is used to extract feature of the original face images, and the multi-class SVM classifier is used to classify the emotions. On the FER-2013 database, the proposed method achieves 68.79% emotion recognition rate, and has obvious advantages compared with most other emotion recognition methods. Next, through the socket communication, the result of the emotion recognition is transferred to the designed seven poker game to realize the emotion linkage between the gamer and his avatar. More importantly, the emotion linkage technology not only helps the gamer to analyze the opponent's psychological state, but also enhances the interaction of the game. It is undoubtedly a new breakthrough in game play that will give gamers a whole new gaming experience.
Keywords
Psychological game; Emotion recognition; CNN; SVM; Emotion linkage technology;
Citations & Related Records
연도 인용수 순위
  • Reference
1 James A. Russell, "The Psychology of Facial Expression", Cambridge University Press, 1997.
2 Mehrabian A, "Communication without words", Psychology Today, Vol.2, No.4, pp.53-56, 1968.
3 Qiu Liwei, "Application of Deep Reinforcement Learning In Video Game Playing", South China University of Technology, 2015.
4 Ke Shan, Di Lu, et al., "Automatic Facial Expression Recognition Based on a Deep Convolutional-Neural-Network Structure", Journal of IEEE, 2017.
5 Liu Kuang, "Facial Expression Recognition with CNN Ensemble", Zhejiang University, 2016.
6 Xu Peng, Bo Hua, "Facial expression recognition based on CNN", Journal of microcomputer its applications, Vol.34, No.12, pp.45-47, 2015.
7 Yichuan Tang, "Deep Learning using Linear Support Vector Machines", Journal of Computer Science, 2013.
8 Shi Xugan, "Facial Expression Recognition Based on Deep Learning", Zhejiang Sci-Tech University, 2015.
9 V Tumen, B Ergen, et al., "Facial Emotion Recognition on a Dataset Using Convolutional Neural Network", Journal of IEEE, 2017.
10 O Arriaga, M Valdenegro-Toro, P Ploger, "Real-time Convolutional Neural Networks for Emotion and Gender Classification, 2017.
11 https://en.wikipedia.org/wiki/Feature_extraction
12 https://en.wikipedia.org/wiki/Deep_learning
13 https://en.wikipedia.org/wiki/Convolutional_neural_network