Automatic Decision-Making Style Recognition Method Using Kinect Technology - Guo, Yu; Liu, Xiaoqian; Wang, Xiaoyang; Zhu, Tingshao; Zhan, Wei (Frontiers in Psychology, 2022)
In recent years, somatosensory interaction technology, represented by Microsoft’s Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style scores. The face data was obtained using the Kinect camera, and the decision-style scores were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style scores. This study adopts a variety of classical machine learning algorithms, including linear regression, support vector machine regression, ridge regression, Bayesian ridge regression, and elastic network. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which is a significantly strong correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.