The assessment of personality traits is now a key part of many important social activities, such as job hunting, accident prevention in transportation, disease treatment, policing, and interpersonal interactions. In a previous study, we predicted personality based on positive images of college students. Although this method achieved a high accuracy, the reliance on positive images alone results in the loss of much personality-related information. Our new findings show that using real-life 2.5D static facial contour images, it is possible to make statistically significant predictions about a wider range of personality traits for both men and women. We address the objective of comprehensive understanding of a person’s personality traits by developing a multiperspective 2.5D hybrid personality-computing model to evaluate the potential correlation between static facial contour images and personality characteristics. Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.

中文翻译：

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基于深度学习的2.5D人脸个性预测

人格特质的评估现在是许多重要社会活动的关键部分，例如求职、交通事故预防、疾病治疗、警务和人际交往。在之前的一项研究中，我们根据大学生的正面形象来预测个性。虽然这种方法取得了很高的准确率，但仅依赖正面图像会导致丢失很多与个性相关的信息。我们的新发现表明，使用现实生活中的 2.5D 静态面部轮廓图像，可以对男性和女性的更广泛的人格特征做出具有统计学意义的预测。我们通过发展多视角 2 来实现全面了解一个人的个性特征的目标。5D混合个性计算模型，用于评估静态面部轮廓图像与个性特征之间的潜在相关性。我们的实验结果表明，由大型标记数据集训练的深度神经网络可以通过 2.5D 静态面部轮廓图像可靠地预测人的多维人格特征，并且预测精度优于之前使用 2D 图像的方法。