LenslessFace: An End-to-End Optimized Lensless System for Privacy-Preserving Face Verification
Xin Cai, Hailong Zhang, Chenchen Wang, Wentao Liu, Jinwei Gu, Tianfan, Xue

TL;DR
LenslessFace introduces an end-to-end optimized lensless system for privacy-preserving face verification, eliminating the need for face reconstruction and enhancing privacy, efficiency, and robustness in lensless imaging applications.
Contribution
It proposes a novel end-to-end approach that directly verifies faces from encoded lensless images, addressing challenges unique to lensless setups and improving over traditional two-stage methods.
Findings
Outperforms two-stage lensless verification methods.
Enhances privacy by avoiding face reconstruction.
Demonstrates robustness in real and simulated environments.
Abstract
Lensless cameras, innovatively replacing traditional lenses for ultra-thin, flat optics, encode light directly onto sensors, producing images that are not immediately recognizable. This compact, lightweight, and cost-effective imaging solution offers inherent privacy advantages, making it attractive for privacy-sensitive applications like face verification. Typical lensless face verification adopts a two-stage process of reconstruction followed by verification, incurring privacy risks from reconstructed faces and high computational costs. This paper presents an end-to-end optimization approach for privacy-preserving face verification directly on encoded lensless captures, ensuring that the entire software pipeline remains encoded with no visible faces as intermediate results. To achieve this, we propose several techniques to address unique challenges from the lensless setup which…
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Taxonomy
TopicsFace recognition and analysis · Biometric Identification and Security · Face and Expression Recognition
MethodsKnowledge Distillation
