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Embedding a face image to a descriptor vector using a deep CNN is a widelyused technique in face recognition. Via several possible training strategies,such embeddings are supposed to capture only identity information. Informationabout the environment (such as background and lighting) or changeable aspectsof the face (such as pose, expression, presence of glasses, hat etc.) should bediscarded since they are not useful for recognition. In this paper, we presenta 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 theID embedding of state-of-the-art face embedding networks (VGGFace2 andArcFace). In fact, these non-ID attributes can be predicted from ID embeddingswith similar accuracy to a prediction from the original image. Going further,we present an optimisation strategy that uses a generative model (specificallyStyleGAN2 for faces) to recover images from an ID embedding. We showphotorealistic inversion from ID embedding to face image in which not only isthe ID realistically reconstructed but the pose, lighting andbackground/apparel to some extent as well.

ID2image: Leakage of non-ID information into face descriptors and inversion from descriptors to images