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Enhancing Face Recognition With Self-Supervised 3D Reconstruction

Attributed to both the development of deep networks and abundant data, automatic face recognition (FR) has quickly reached human-level capacity in the past few years. However, the FR problem is not perfectly solved in case of uncontrolled illumination and pose. In this paper, we propose to enhance face recognition with a bypass of self-supervised 3D reconstruction, which enforces the neural backbone to focus on the identity-related depth and albedo information while neglects the identity-irrelevant pose and illumination information. Specifically, inspired by the physical model of image formation, we improve the backbone FR network by introducing a 3D face reconstruction loss with two auxiliary networks. The first one estimates the pose and illumination from the input face image while the second one decodes the canonical depth and albedo from the intermediate feature of the FR backbone network. The whole network is trained in end-to-end manner with both classic face identification loss and the loss of 3D face reconstruction with the physical parameters. In this way, the self-supervised reconstruction acts as a regularization that enables the recognition network to understand faces in 3D view, and the learnt features are forced to encode more information of canonical facial depth and albedo, which is more intrinsic and beneficial to face recognition. Extensive experimental results on various face recognition benchmarks show that, without any cost of extra annotations and computations, our method outperforms state-of-the-art ones. Moreover, the learnt representations can also well generalize to other face-related downstream tasks such as the facial attribute recognition with limited labeled data.

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