Zhao Jian, Zhang Miao, He Chen, Xie Xie, Li Jiaming
School of Information Science and Technology, Northwest University, Xi'an, 710127 China.
Cogn Neurodyn. 2020 Oct;14(5):643-656. doi: 10.1007/s11571-020-09591-9. Epub 2020 Jun 4.
Facial attractiveness is an important research direction of genetic psychology and cognitive psychology, and its results are significant for the study of face evolution and human evolution. However, previous studies have not put forward a comprehensive evaluation system of facial attractiveness. Traditionally, the establishment of facial attractiveness evaluation system was based on facial geometric features, without facial skin features. In this paper, combined with big data analysis, evaluation of face in real society and literature research, we found that skin also have a significant impact on facial attractiveness, because skin could reflect age, wrinkles and healthful qualities, thus affected the human perception of facial attractiveness. Therefore, we propose a comprehensive and novel facial attractiveness evaluation system based on face shape structural features, facial structure features and skin texture feature. In order to apply face shape structural features to the evaluation of facial attractiveness, the classification of face shape is the first step. Face image dataset is divided according to face shape, and then facial structure features and skin texture features that represent facial attractiveness are extracted and fused. The machine learning algorithm with the best prediction performance is selected in the face shape structural subsets to predict facial attractiveness. Experimental results show that the facial attractiveness evaluation performance can be improved by the method based on classification of face shape and multi-features fusion, the facial attractiveness scores obtained by the proposed system correlates better with human ratings. Our evaluation system can help people project their cognition of facial attractiveness into artificial agents they interact with.
面部吸引力是遗传心理学和认知心理学的一个重要研究方向，其研究结果对于面部进化和人类进化的研究具有重要意义。然而，以往的研究尚未提出一个全面的面部吸引力评价体系。传统上，面部吸引力评价体系的建立基于面部几何特征，而未考虑面部皮肤特征。本文结合大数据分析、现实社会中的面部评价以及文献研究，发现皮肤对面部吸引力也有显著影响，因为皮肤能够反映年龄、皱纹和健康状况，从而影响人类对面部吸引力的感知。因此，我们提出了一种基于面部形状结构特征、面部结构特征和皮肤纹理特征的全面且新颖的面部吸引力评价体系。为了将面部形状结构特征应用于面部吸引力评价，对面部形状进行分类是第一步。根据面部形状对面部图像数据集进行划分，然后提取并融合代表面部吸引力的面部结构特征和皮肤纹理特征。在面部形状结构子集中选择预测性能最佳的机器学习算法来预测面部吸引力。实验结果表明，基于面部形状分类和多特征融合的方法能够提高面部吸引力评价性能，所提出的系统获得的面部吸引力得分与人类评分的相关性更好。我们的评价体系能够帮助人们将对面部吸引力的认知投射到与其交互的智能体中。
