Face recognition has been used more and more in real world applications in recent years. However, when the skin color bias is coupled with intra-personal variations like harsh illumination, the face recognition task is more likely to fail, even during human inspection. Face normalization methods try to deal with such challenges by removing intra-personal variations from an input image while keeping the identity the same. However, most face normalization methods can only remove one or two variations and ignore dataset biases such as skin color bias. The outputs of many face normalization methods are also not realistic to human observers. In this work, a style based face normalization model (StyleFNM) is proposed to remove most intra-personal variations including large changes in pose, bad or harsh illumination, low resolution, blur, facial expressions, and accessories like sunglasses among others. The dataset bias is also dealt with in this paper by controlling a pretrained GAN to generate a balanced dataset of passport-like images. The experimental results show that StyleFNM can generate more realistic outputs and can improve significantly the accuracy and fairness of face recognition systems.

翻译：近年来，人脸识别在现实世界应用中的使用日益广泛。然而，当肤色偏差与诸如强光照等个体内变化相结合时，人脸识别任务更可能失败，即使是在人工检查中也是如此。人脸归一化方法试图通过从输入图像中移除个体内变化同时保持身份不变来应对此类挑战。然而，大多数人脸归一化方法只能移除一两种变化，而忽略了如肤色偏差等数据集偏差。许多人脸归一化方法的输出对观察者而言也不够真实。本文提出了一种基于风格的人脸归一化模型（StyleFNM），以移除大多数个体内变化，包括姿态的大幅变化、不良或强光照、低分辨率、模糊、面部表情以及太阳镜等配饰。本文还通过控制预训练的生成对抗网络（GAN）生成平衡的护照类图像数据集来处理数据集偏差。实验结果表明，StyleFNM能够生成更真实的输出，并显著提升人脸识别系统的准确性和公平性。