50 Years of Automated Face Recognition
Minchul Kim, Anil Jain, Xiaoming Liu

TL;DR
This paper reviews 50 years of automated face recognition, highlighting technological advances, dataset growth, and recent near-perfect large-scale identification performance, while discussing future challenges and directions.
Contribution
It provides a comprehensive historical overview and analysis of technological progress in face recognition, emphasizing the impact of dataset scale and recent state-of-the-art results.
Findings
Near-perfect large-scale identification accuracy achieved
Leading algorithm reports FNIR of 0.15% at FPIR of 0.001
Dataset expansion correlates with performance gains
Abstract
Over the past five decades, automated face recognition (FR) has progressed from handcrafted geometric and statistical approaches to advanced deep learning architectures that now approach, and in many cases exceed, human performance. This paper traces the historical and technological evolution of FR, encompassing early algorithmic paradigms through to contemporary neural systems trained on extensive real and synthetically generated datasets. We examine pivotal innovations that have driven this progression, including advances in dataset construction, loss function formulation, network architecture design, and feature fusion strategies. Furthermore, we analyze the relationship between data scale, diversity, and model generalization, highlighting how dataset expansion correlates with benchmark performance gains. Recent systems have achieved near-perfect large-scale identification accuracy,…
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Taxonomy
TopicsFace recognition and analysis · Face and Expression Recognition · Biometric Identification and Security
