Humans are generally thought to be experts at face recognition, and yet identity perception for unfamiliar faces is surprisingly poor compared to that for familiar faces. Prior theoretical work has argued that unfamiliar face identity perception suffers because the majority of identity-invariant visual variability is idiosyncratic to each identity, and thus, each face identity must be learned essentially from scratch. Using a high-performing deep convolutional neural network, we evaluate this claim by examining the effects of visual experience in untrained, object-expert and face-expert networks. We found that only face training led to substantial generalization in an identity verification task of novel unfamiliar identities. Moreover, generalization increased with the number of previously learned identities, highlighting the generality of identity-invariant information in face images. To better understand how familiarity builds upon generic face representations, we simulated familiarization with face identities by fine-tuning the network on images of the previously unfamiliar identities. Familiarization produced a sharp boost in verification, but only approached ceiling performance in the networks that were highly trained on faces. Moreover, in these face-expert networks, the sharp familiarity benefit was seen only at the identity-based output probability layer, and did not depend on changes to perceptual representations; rather, familiarity effects required learning only at the level of identity readout from a fixed expert representation. Our results thus reconcile the existence of a large familiar face advantage with claims that both familiar and unfamiliar face identity processing depend on shared expert perceptual representations.

人类通常被认为是面部识别方面的专家，但与熟悉的面孔相比，对陌生面孔的身份感知却出奇地差。先前的理论工作认为，不熟悉的面部身份感知会受到影响，因为大多数身份不变的视觉变化对于每个身份来说都是特殊的，因此，每个面部身份基本上必须从头开始学习。使用高性能深度卷积神经网络，我们通过检查未经训练的对象专家和面部专家网络中视觉体验的影响来评估这一说法。我们发现，只有面部训练才能在新的陌生身份的身份验证任务中产生实质性的泛化。此外，泛化随着先前学习的身份数量的增加而增加，突出了面部图像中身份不变信息的普遍性。为了更好地理解熟悉度如何建立在通用面部表示的基础上，我们通过对先前不熟悉的身份的图像微调网络来模拟对面部身份的熟悉度。熟悉程度极大地提高了验证能力，但仅接近经过严格面部训练的网络的性能上限。此外，在这些面部专家网络中，仅在基于身份的输出概率层才能看到明显的熟悉度收益，并且不依赖于感知表示的变化；相反，熟悉效应只需要在从固定的专家代表中读出身份的水平上进行学习。因此，我们的结果将大量熟悉面孔优势的存在与熟悉和陌生面孔身份处理都依赖于共享的专家感知表征的主张相一致。