人脸图像到护照格式的包容性归一化
Inclusive normalization of face images to passport format
AI总结：
提出StyleFNM模型，通过风格化归一化移除人脸图像中的多种个体内部变化并处理肤色偏差，生成更真实的护照类图像，显著提升人脸识别的准确性与公平性。
摘要：
近年来，人脸识别在现实世界应用中的使用越来越广泛。然而，当肤色偏差与严酷光照等个体内部变化相结合时，人脸识别任务更有可能失败，即使在人工检查期间也是如此。人脸归一化方法试图通过从输入图像中移除个体内部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种或两种变化，并忽略了数据集偏差（如肤色偏差）。许多人脸归一化方法的输出对人类观察者来说也不够真实。在这项工作中，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部变化，包括姿态的大幅变化、恶劣或严酷的光照、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可以生成更真实的输出，并能显著提高人脸识别系统的准确性和公平性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
