Abstract
Advancement of machine learning techniques, combined with the availability of large-scale datasets, has significantly improved the accuracy and efficiency of facial recognition. Modern facial recognition systems are trained using large face datasets collected from diverse individuals or public repositories. However, for training, these datasets are often replicated and stored in multiple workstations, resulting in data replication, which complicates database management and oversight. Currently, once a user submits their face for dataset preparation, they lose control over how their data is used, raising significant privacy and ethical concerns. This paper introduces VOIDFace, a novel framework for facial recognition systems that addresses two major issues. First, it eliminates the need of data replication and improves data control to securely store training face data by using visual secret sharing. Second, it proposes a patch-based multi-training network that uses this novel training data storage mechanism to develop a robust, privacy-preserving facial recognition system. By integrating these advancements, VOIDFace aims to improve the privacy, security, and efficiency of facial recognition training, while ensuring greater control over sensitive personal face data. VOIDFace also enables users to exercise their Right-To-Be-Forgotten property to control their personal data. Experimental evaluations on the VGGFace2 dataset show that VOIDFace provides Right-To-Be-Forgotten, improved data control, security, and privacy while maintaining competitive facial recognition performance. Code is available at: this https URL
Abstract (translated)
机器学习技术的进步，结合大规模数据集的可用性，显著提高了面部识别的准确性和效率。现代面部识别系统使用从各种个体或公共存储库收集的大规模人脸数据进行训练。然而，在训练过程中，这些数据集通常被复制并存储在多个工作站上，导致了数据冗余，并且使数据库管理变得复杂化。目前的情况是，一旦用户提交他们的面部信息用于数据集准备，他们就失去了对其数据使用方式的控制权，这引发了重要的隐私和伦理问题。 本文介绍了VOIDFace框架，这是一个面向面部识别系统的创新方案，旨在解决两大主要问题：首先，它消除了数据复制的需求，并通过采用视觉秘密共享技术来提高数据管理的安全性，以安全地存储训练用的人脸数据。其次，它提出了一种基于补丁的多训练网络，利用这种新颖的数据存储机制来开发一种既强大又保护隐私的面部识别系统。 通过整合这些改进措施，VOIDFace旨在提升面部识别系统的隐私、安全性和效率，并确保对敏感个人面部数据拥有更大的控制权。此外，VOIDFace还允许用户行使“被遗忘的权利”，即有权控制和管理自己的个人信息。 在VGGFace2数据集上的实验评估表明，VOIDFace不仅能提供符合“被遗忘的权利”的能力，还能改进数据控制、安全性和隐私保护，并且仍然保持竞争力的面部识别性能。 代码可在以下网址获取：[此链接](https://this-url.com)
URL
https://arxiv.org/abs/2508.07960
