Substantial improvements have been achieved in the field of face recognition due to the successful application of deep neural networks. However, existing methods are sensitive to both the quality and quantity of the training data. Despite the availability of large-scale datasets, the long tail data distribution induces strong biases in model learning. In this paper, we present a Face Augmentation Generative Adversarial Network (FA-GAN) to reduce the influence of imbalanced deformation attribute distributions. We propose to decouple these attributes from the identity representation with a novel hierarchical disentanglement module. Moreover, Graph Convolutional Networks (GCNs) are applied to recover geometric information by exploring the interrelations among local regions to guarantee the preservation of identities in face data augmentation. Extensive experiments on face reconstruction, face manipulation, and face recognition demonstrate the effectiveness and generalization ability of the proposed method.

中文翻译：

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FA-GAN：用于变形不变人脸识别的人脸增强 GAN


由于深度神经网络的成功应用，人脸识别领域取得了实质性的进步。然而，现有方法对训练数据的质量和数量都很敏感。尽管存在大规模数据集，但长尾数据分布会导致模型学习产生强烈偏差。在本文中，我们提出了一种人脸增强生成对抗网络（FA-GAN）来减少不平衡变形属性分布的影响。我们建议使用一种新颖的分层解缠模块将这些属性与身份表示解耦。此外，图卷积网络（GCN）通过探索局部区域之间的相互关系来恢复几何信息，以保证人脸数据增强中身份的保存。人脸重建、人脸操作和人脸识别的大量实验证明了该方法的有效性和泛化能力。