KINECT RANDOM DOT PATTERN SOFTWARE

Microsoft HD Face Basic SDK ( Microsoft, 2014a) obtained a total of 1347 3D facial points. This study aims to explore the effective use of Kinect devices to recognize decision-making styles.

Machine learning and data mining technology have achieved significant success in many engineering fields, including classification, regression, and clustering ( Pan and Yang, 2010). A model is constructed based on input (for example, in this paper, face data obtained by a Kinect) to manipulate such algorithms for data-driven prediction or decision making ( Wei and Jia, 2017). Machine learning involves algorithms that can learn and make predictions based on data.

KINECT RANDOM DOT PATTERN SOFTWARE

With the latest version of the Kinect Software Development Kit (SDK) 2.0, the device can automatically isolate the face from the rest of the environment and use a grid structure of more than 1,000 3D coordinate points to represent the face ( Zhang et al., 2016). Recently, Kinect devices have has been widely used to support scientific research. Microsoft’s Kinect is a reliable 3D image capture hardware platform that has been widely used in multimedia interactions, such as games, education, and healthcare training. To address these issues, this research proposes a new method to measure decision-making style. However, scale assessment is time-consuming and is not efficient enough in practical application scenarios such as enterprises. The GDMS scale has been applied in different countries, populations, and age groups ( Thunholm, 2008 Baiocco et al., 2009 Luke et al., 2012 Bavol’ar and Orosova, 2015 Delaney et al., 2015 Fischer et al., 2015 Alacreu-Crespo et al., 2019). Among them, the GDMS measures five dimensions of decision-making style, i.e., spontaneous, avoidant, rational, dependent, and intuitive ( Scott and Bruce, 1995). To identify an individual’s decision-making style, researchers and companies generally use scales or questionnaires, such as Compensation Strategy Questionnaire ( Zakay, 1990), General Decision-Making Style Questionnaire (GDMS) ( Scott and Bruce, 1995), Decision-Making Inventory Questionnaire ( Johnson, 1978), and Assessment of Career Decision-Making Inventory Questionnaire (ACDM) ( Harren, 1979). The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.ĭecision-making style is a habitual or unique behavior pattern that has an important influence on consumer behavior, career development, management, business, and other fields. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The experimental results show that the linear regression model returns the best results. 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.

1Institute of Psychology, Chinese Academy of Sciences, Beijing, China.

Yu Guo 1,2, Xiaoqian Liu 1,2*, Xiaoyang Wang 1,2, Tingshao Zhu 1,2 and Wei Zhan 3

Kinect random dot pattern