Ryali Chaitanya K, Wang Xiaotian, Yu Angela J
Department of Computer Science and Engineering, University of California, San Diego La Jolla, CA 92093 USA.
Department of Electrical and Computer Engineering, University of California, San Diego La Jolla, CA 92093 USA.
Cogsci. 2020 Jul-Aug;42:1080-1086.
Face processing plays a critical role in human social life, from differentiating friends from enemies to choosing a life mate. In this work, we leverage various computer vision techniques, combined with human assessments of similarity between pairs of faces, to investigate human face representation. We find that combining a shape- and texture-feature based model (Active Appearance Model) with a particular form of metric learning, not only achieves the best performance in predicting human similarity judgments on held-out data (both compared to other algorithms and to humans), but also performs better or comparable to alternative approaches in modeling human social trait judgment (e.g. trustworthiness, attractiveness) and affective assessment (e.g. happy, angry, sad). This analysis yields several scientific findings: (1) facial similarity judgments rely on a relative small number of facial features (8-12), (2) race- and gender-informative features play a prominent role in similarity perception, (3) similarity-relevant features alone are insufficient to capture human face representation, in particular some affective features missing from similarity judgments are also necessary for constructing the complete psychological face representation.
面部处理在人类社会生活中起着至关重要的作用，从区分敌友到选择人生伴侣。在这项工作中，我们利用各种计算机视觉技术，并结合人类对成对面孔之间相似度的评估，来研究人类面部表征。我们发现，将基于形状和纹理特征的模型（主动外观模型）与特定形式的度量学习相结合，不仅在预测对留出数据的人类相似度判断方面取得了最佳性能（与其他算法以及人类相比），而且在对人类社会特质判断（如可信度、吸引力）和情感评估（如开心、愤怒、悲伤）进行建模时，其表现优于或等同于其他替代方法。该分析得出了几个科学发现：（1）面部相似度判断依赖于相对较少的面部特征（8 - 12个）；（2）具有种族和性别信息的特征在相似度感知中起着突出作用；（3）仅与相似度相关的特征不足以捕捉人类面部表征，特别是相似度判断中缺失的一些情感特征对于构建完整的心理面部表征也是必要的。
