FunFace: 用于人脸识别的特征效用和范数估计
FunFace: Feature Utility and Norm Estimation for Face Recognition
AI总结：
本文提出FunFace，一种结合生物效用和范数估计的自适应边缘损失函数，用于提升人脸识别模型在高质量和低质量样本上的性能。
摘要：
人脸识别（FR）广泛应用于娱乐、银行、安全和监控等领域，这些应用依赖于FR模型在各种设置中具有鲁棒性和良好的性能。为了实现这一点，最先进的FR模型通常使用具有表达性的自适应边缘损失函数，将特征范数与与样本质量相关的概念（如可识别性和感知图像质量）联系起来。最近，通过开发面部图像质量评估（FIQA）技术，生物效用已成为面部图像质量的首选度量标准，并已证明比更以人类为中心的方面（如分辨率、模糊和照明）更能预测样本对人脸识别的有用性。虽然通过特征范数表达的图像质量与生物效用有一定的相关性，但并不能完全涵盖所有效用方面。为此，我们提出了一种新的自适应边缘损失，FunFace（通过效用和范数估计的人脸识别），它将通过确定性比率估计的生物效用纳入自适应边缘中，借鉴了AdaFace的灵感。我们显示，FunFace（用于训练人脸识别模型）在包含高质量样本的基准上与最先进的FR模型取得竞争性结果，在低质量基准上则超越了它们。
英文摘要：
Face Recognition (FR) is used in a variety of application domains, from entertainment and banking to security and surveillance. Such applications rely on the FR model to be robust and perform well in a variety of settings. To achieve this, state-of-the-art FR models typically use expressive adaptive margin loss functions, which tie the feature norm to concepts related to sample quality, such as recognizability and perceptual image quality. Recently, through the development of Face Image Quality Assessment (FIQA) techniques, biometric utility has become the preferred measure of face-image quality and has been shown to be a better predictor of the usefulness of samples for face recognition compared to more human-centric aspects, such as resolution, blur, and lighting, tied to general image quality. While image quality expressed through feature norms exhibits a certain level of correlation with biometric utility, it does not fully encapsulate all aspects of utility. To address this point, we propose a new adaptive margin loss, FunFace (Face Recognition Through Utility and Norm Estimation), which incorporates biometric utility, estimated by the Certainty Ratio, into the adaptive margin, taking inspiration from AdaFace. We show that FunFace (when used to train a face recognition model) achieves competitive results to other state-of-the-art FR models on benchmarks containing high-quality samples, while surpassing them on low quality benchmarks.
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。
