Boosting Deep Face Recognition via Disentangling Appearance and Geometry

Ali Dabouei

Abstract:

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.