This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. 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 score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. In this paper, an automatic decision-style recognition method is proposed. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. 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. 3Information Science Research Institute, China Electronics Technology Group Corporation, Beijing, China.2Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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