  In this paper, we propose a framework for disentangling the appearance and
geometry representations in the face recognition task. To provide supervision
for this aim, we generate geometrically identical faces by incorporating
spatial transformations. We demonstrate that the proposed approach enhances the
performance of deep face recognition models by assisting the training process
in two ways. First, it enforces the early and intermediate convolutional layers
to learn more representative features that satisfy the properties of
disentangled embeddings. Second, it augments the training set by altering faces
geometrically. Through extensive experiments, we demonstrate that integrating
the proposed approach into state-of-the-art face recognition methods
effectively improves their performance on challenging datasets, such as LFW,
YTF, and MegaFace. Both theoretical and practical aspects of the method are
analyzed rigorously by concerning ablation studies and knowledge transfer
tasks. Furthermore, we show that the knowledge leaned by the proposed method
can favor other face-related tasks, such as attribute prediction.
