The small amount of training data for many state-of-the-art deep learning-based Face Recognition (FR) systems causes a marked deterioration in their performance. Although a considerable amount of research has addressed this issue by inventing new data augmentation techniques, using either input space transformations or Generative Adversarial Networks (GAN) for feature space augmentations, these techniques have yet to satisfy expectations. In this paper, we propose an approach named the Face Representation Augmentation (FRA) for augmenting face datasets. To the best of our knowledge, FRA is the first method that shifts its focus towards manipulating the face embeddings generated by any face representation learning algorithm to create new embeddings representing the same identity and facial emotion but with an altered posture. Extensive experiments conducted in this study convince of the efficacy of our methodology and its power to provide noiseless, completely new facial representations to improve the training procedure of any FR algorithm. Therefore, FRA can help the recent state-of-the-art FR methods by providing more data for training FR systems. The proposed method, using experiments conducted on the Karolinska Directed Emotional Faces (KDEF) dataset, improves the identity classification accuracies by 9.52 %, 10.04 %, and 16.60 %, in comparison with the base models of MagFace, ArcFace, and CosFace, respectively.

翻译：摘要：许多基于深度学习的最新面部识别系统因训练数据量不足而导致性能显著下降。尽管大量研究通过提出新的数据增强技术（包括输入空间变换或生成对抗网络用于特征空间增强）来应对该问题，但这些技术尚未达到预期效果。本文提出一种名为面部表征增强的方法用于扩充人脸数据集。据我们所知，FRA是首个将重心转向操控任何人脸表征学习算法生成的面部嵌入的方法，通过创建具有相同身份与面部情感但姿态改变的新嵌入。本研究开展的广泛实验证实了该方法的有效性及其提供无噪声、全新面部表征以优化任何面部识别算法训练流程的能力。因此，FRA可通过为训练面部识别系统提供更多数据来助力最新前沿的面部识别方法。在Karolinska定向情感面孔数据集上进行的实验表明，与MagFace、ArcFace和CosFace基准模型相比，所提方法将身份分类准确率分别提升了9.52%、10.04%和16.60%。