# LenslessFace: An End-to-End Optimized Lensless System for Privacy-Preserving Face Verification

arXiv: https://arxiv.org/abs/2406.04129 · HTML: https://mlsanity.com/paper/2406.04129/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Bridging Composite and Real: Towards End-to-end Deep Image Matting](https://mlsanity.com/paper/2010.16188/) (2020)
- [One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models](https://mlsanity.com/paper/1703.09912/) (2017)
- [DiffuserCam: Lensless Single-exposure 3D Imaging](https://mlsanity.com/paper/1710.02134/) (2017)
- [YOLO5Face: Why Reinventing a Face Detector](https://mlsanity.com/paper/2105.12931/) (2021)
- [FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements](https://mlsanity.com/paper/2010.15440/) (2020)
- [Privacy-Preserving Action Recognition using Coded Aperture Videos](https://mlsanity.com/paper/1902.09085/) (2019)
- [Lensless Imaging by Compressive Sensing](https://mlsanity.com/paper/1305.7181/) (2013)
- [Privacy-Enhancing Optical Embeddings for Lensless Classification](https://mlsanity.com/paper/2211.12864/) (2022)
- [Lensless Imaging with Compressive Ultrafast Sensing](https://mlsanity.com/paper/1610.05834/) (2016)

## Built on by (most cited first, 2 of 2)

- [LFD: Enabling Real-World Lensless Face Recognition with a Large-Scale Dataset](https://mlsanity.com/paper/2607.10094/) (2026)
- [Lensless Gaze Is Not Private by Default: Auditing Identity Leakage Across Disclosure Surfaces](https://mlsanity.com/paper/2609.09188/) (2026)

## Abstract (arXiv)

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 precludes traditional face detection and alignment. Specifically, we propose a face center alignment scheme, an augmentation curriculum to build robustness against variations, and a knowledge distillation method to smooth optimization and enhance performance. Evaluations under both simulation and real environment demonstrate our method outperforms two-stage lensless verification while enhancing privacy and efficiency. Project website: urllenslessface.github.io.