Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
Front Public Health. 2022 Sep 9;10:1001828. doi: 10.3389/fpubh.2022.1001828. eCollection 2022.
The personality assessment is in high demand in various fields and is becoming increasingly more important in practice. In recent years, with the rapid development of machine learning technology, the integration research of machine learning and psychology has become a new trend. In addition, the technology of automatic personality identification based on facial analysis has become the most advanced research direction in large-scale personality identification technology. This study proposes a method to automatically identify the Big Five personality traits by analyzing the facial movement in ordinary videos. In this study, we collected a total of 82 sample data. First, through the correlation analysis between facial features and personality scores, we found that the points from the right jawline to the chin contour showed a significant negative correlation with agreeableness. Simultaneously, we found that the movements of the left cheek's outer contour points in the high openness group were significantly higher than those in the low openness group. This study used a variety of machine learning algorithms to build the identification model on 70 key points of the face. Among them, the CatBoost regression algorithm has the best performance in the five dimensions, and the correlation coefficients between the model prediction results and the scale evaluation results are about medium correlation (0.37-0.42). Simultaneously, we executed the Split-Half reliability test, and the results showed that the reliability of the experimental method reached a high-reliability standard (0.75-0.96). The experimental results further verify the feasibility and effectiveness of the automatic assessment method of Big Five personality traits based on individual facial video analysis.
人格评估在各个领域的需求都很高，在实践中变得越来越重要。近年来，随着机器学习技术的飞速发展，机器学习与心理学的融合研究已成为一个新趋势。此外，基于面部分析的自动人格识别技术已成为大规模人格识别技术中最先进的研究方向。本研究提出了一种通过分析普通视频中的面部运动来自动识别大五人格特质的方法。在这项研究中，我们总共收集了 82 个样本数据。首先，通过面部特征与人格得分的相关分析，我们发现从右侧下颌线到下巴轮廓的点与宜人性呈显著负相关。同时，我们发现高开放性组左侧脸颊外轮廓点的运动幅度明显高于低开放性组。本研究使用多种机器学习算法在面部的 70 个关键点上构建识别模型。其中，CatBoost 回归算法在五个维度上的性能最佳，模型预测结果与量表评估结果之间的相关系数约为中等相关（0.37-0.42）。同时，我们执行了 Split-Half 可靠性测试，结果表明实验方法的可靠性达到了高可靠性标准（0.75-0.96）。实验结果进一步验证了基于个体面部视频分析的大五人格特质自动评估方法的可行性和有效性。
