Embedding a face image to a descriptor vector using a deep CNN is a widely
used technique in face recognition. Via several possible training strategies,
such embeddings are supposed to capture only identity information. Information
about the environment (such as background and lighting) or changeable aspects
of the face (such as pose, expression, presence of glasses, hat etc.) should be
discarded since they are not useful for recognition. In this paper, we present
a surprising result that this is not the case. We show that non-ID attributes,
as well as landmark positions and the image histogram can be recovered from the
ID embedding of state-of-the-art face embedding networks (VGGFace2 and
ArcFace). In fact, these non-ID attributes can be predicted from ID embeddings
with similar accuracy to a prediction from the original image. Going further,
we present an optimisation strategy that uses a generative model (specifically
StyleGAN2 for faces) to recover images from an ID embedding. We show
photorealistic inversion from ID embedding to face image in which not only is
the ID realistically reconstructed but the pose, lighting and
background/apparel to some extent as well.

本文研究发现即使经过多种训练策略，使用深度卷积神经网络将人脸图像转为描述矢量的 Embedding 仍可恢复非身份（non-ID）属性，如地标位置和图像直方图，并且我们还使用生成模型进一步优化，实现了从 ID embedding 到面部图像的逆向重建，其中身份、姿态、光线和背景 / 服饰等非身份属性都得到了真实的重建。