Face Space Representations in Deep Convolutional Neural Networks

Inspired by the primate visual system, deep convolutional neural networks (DCNNs) have made impressive progress on the complex problem of recognizing faces across variations of viewpoint, illumination, expression, and appearance. This generalized face recognition is a hallmark of human recognition for familiar faces. Despite the computational advances, the visual nature of the face code that emerges in DCNNs is poorly understood. We review what is known about these codes, using the long-standing metaphor of a 'face space' to ground them in the broader context of previous-generation face recognition algorithms. We show that DCNN face representations are a fundamentally new class of visual representation that allows for, but does not assure, generalized face recognition.