Abstract
A low 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 a novel method, named the Face Representation Augmentation (FRA) algorithm, 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 in order to generate new embeddings representing the same identity and facial emotion but with an altered posture. Extensive experiments conducted in this study convince 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 is able to 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.
Abstract (translated)
许多先进的深度学习基于人脸识别(FR)系统的训练数据量相对较小,这导致其表现显著恶化。虽然大量的研究已经发明了新的数据增强技术,使用输入空间变换或生成对抗网络(GAN)来增强特征空间数据,但这些技术尚未满足期望。在本文中,我们提出了一种新方法,称为“人脸表示增强”(FRA)算法,用于增加人脸数据集。据我们所知,FRA是第一种将注意力转向操纵任何人脸表示学习算法生成的人脸嵌入以生成新的嵌入代表相同身份和面部情感但不同姿势的方法。在本研究中,我们通过广泛的实验证明,我们的方法和提供无噪声、全新的人脸表示可以提高任何FR算法的学习过程。因此,FRA可以帮助最近先进的FR方法,通过提供训练FR系统的更多数据。我们提出的方法和使用Karolinska指导的情感人脸(KDEF)数据集的实验相比, identity分类准确率提高了9.52%、10.04%和16.60%。
URL
https://arxiv.org/abs/2301.11986
