关键词: 博弈论, 特征选择, 步态融合, 支付函数

Abstract: Recently, fusion-based gait recognition has become one of the hottest topics in the domain of biometrics recognition. There are not only complementary relation among different information sources, but also redundancy and conflict. A novel fusion method based on game selection model is proposed from the feature selection standpoint. Firstly, the features which have redundant characteristics are defined as the same player, and the features which have characteristics of complementary are defined as the other player. Then payoff function is constructed using entropy and Mutual Information（MI）, which can reflect the conflict of credibility between players. At the same time, the payoff value reflects the distinguishing power of the classification. The higher the payoff value is, the stronger the classification ability of features are. Finally, the equilibrium solution is obtained from the payoff matrix by maximin principle, and its corresponding position is the optimal subset. Through the exhaustive comparison experiments on CMU and CASIA data sets, the effectiveness of the proposed method is verified. Experimental results show that the best performance of recognition can be obtained by using less computational complexity.

Key words: game theory, feature selection, fusion-based gait recognition, payoff function