Abstract
Over the past 50 years, automated face recognition has evolved from rudimentary, handcrafted systems into sophisticated deep learning models that rival and often surpass human performance. This paper chronicles the history and technological progression of FR, from early geometric and statistical methods to modern deep neural architectures leveraging massive real and AI-generated datasets. We examine key innovations that have shaped the field, including developments in dataset, loss function, neural network design and feature fusion. We also analyze how the scale and diversity of training data influence model generalization, drawing connections between dataset growth and benchmark improvements. Recent advances have achieved remarkable milestones: state-of-the-art face verification systems now report False Negative Identification Rates of 0.13% against a 12.4 million gallery in NIST FRVT evaluations for 1:N visa-to-border matching. While recent advances have enabled remarkable accuracy in high- and low-quality face scenarios, numerous challenges persist. While remarkable progress has been achieved, several open research problems remain. We outline critical challenges and promising directions for future face recognition research, including scalability, multi-modal fusion, synthetic identity generation, and explainable systems.
Abstract (translated)
在过去50年里，自动面部识别技术已经从原始的手工制作系统发展成为复杂的深度学习模型，并且在许多情况下其性能已经超过人类。本文回顾了面部识别（FR）的历史和技术进步，从早期的几何和统计方法到现代利用大量真实数据和人工智能生成数据集的深层神经网络架构的发展历程。我们分析了塑造这一领域的重要创新，包括数据集、损失函数、神经网络设计以及特征融合等方面的发展。 文中还探讨了训练数据规模和多样性如何影响模型泛化能力，并且讨论了随着数据集的增长，基准测试改进之间的联系。最近的进步已经取得了令人瞩目的成果：在NIST FRVT评估中，针对1:N的签证到边境匹配场景下，最先进的面部验证系统报告的假阴性识别率（FNIR）仅为0.13%，而其对比的数据集包含1240万张图像。 尽管近期进展已经在高质量和低质量人脸场景中实现了显著的准确性提升，但仍存在许多挑战。虽然已经取得了令人瞩目的进步，但仍然有许多开放的研究问题尚未解决。我们概述了未来面部识别研究中的关键挑战及有前景的方向，包括可扩展性、多模式融合、合成身份生成以及可解释系统等方面。
URL
https://arxiv.org/abs/2505.24247
