Intact recognition of familiar faces is critical for appropriate social interactions. Thus, the human face processing system should be optimized for familiar face recognition. Blauch et al. (2020) used face recognition deep convolutional neural networks (DCNNs) that are trained to maximize recognition of the trained (familiar) identities, to model human unfamiliar and familiar face recognition. In line with this model, we discuss behavioral, neuroimaging and computational findings that indicate that human face recognition develops from the generation of identity-specific concepts of familiar faces that are learned in a supervised manner, to the generation of view-invariant identity-general perceptual representations. Face-trained DCNNs seem to share some fundamental similarities with this framework.

完整识别熟悉的面孔对于适当的社交互动至关重要。因此，应该优化人脸处理系统以熟悉的人脸识别。Blauch等。（2020）使用人脸识别深度卷积神经网络（DCNN），对它们进行训练，以最大限度地提高对受过训练的（熟悉的）身份的识别，从而对人类不熟悉和熟悉的人脸识别进行建模。根据该模型，我们讨论了行为，神经影像和计算结果，这些结果表明，人脸识别已从以监督方式学习的熟悉面孔的特定于身份的概念的生成发展为视图不变的一般身份的生成感性表征。脸部训练的DCNN似乎与此框架有一些基本的相似之处。