Abstract
Face recognition systems rely on learning highly discriminative and compact identity clusters to enable accurate retrieval. However, as with other surveillance-oriented technologies, such systems raise serious privacy concerns due to their potential for unauthorized identity tracking. While several works have explored machine unlearning as a means of privacy protection, their applicability to face retrieval - especially for modern embedding-based recognition models - remains largely unexplored. In this work, we study the problem of face identity unlearning for retrieval systems and present its inherent challenges. The goal is to make selected identities unretrievable by dispersing their embeddings on the hypersphere and preventing the formation of compact identity clusters that enable re-identification in the gallery. The primary challenge is to achieve this forgetting effect while preserving the discriminative structure of the embedding space and the retrieval performance of the model for the remaining identities. To address this, we evaluate several existing approximate class unlearning methods (e.g., Random Labeling, Gradient Ascent, Boundary Unlearning, and other recent approaches) in the context of face retrieval and propose a simple yet effective dispersion-based unlearning approach. Extensive experiments on standard benchmarks (VGGFace2, CelebA) demonstrate that our method achieves superior forgetting behavior while preserving retrieval utility.
Abstract (translated)
面部识别系统依赖于学习高度判别性和紧凑的身份聚类，以实现准确的检索。然而，与其他监控技术一样，这类系统由于其潜在的未经授权的身份追踪能力而引发了严重的隐私问题。尽管已有几项研究探索了机器遗忘作为隐私保护手段的应用，但它们在面部检索（尤其是针对现代嵌入式识别模型）中的适用性仍然鲜有探讨。 在这项工作中，我们研究了面向检索系统的面部身份遗忘问题，并展示了其固有的挑战。我们的目标是通过分散选定身份的嵌入以使其无法被检索，在超球面上防止紧凑的身份聚类形成，从而阻止重新识别。主要挑战在于在保留嵌入空间的判别性结构和模型对剩余身份的检索性能的同时实现这种忘记效果。 为解决这一问题，我们评估了几种现有的近似类别遗忘方法（如随机标签法、梯度上升法、边界遗忘法及其他最近的方法）在面部检索中的应用，并提出了一种简单而有效的基于分散的遗忘策略。我们在标准基准测试集（VGGFace2和CelebA）上的广泛实验表明，我们的方法能够实现更优的忘记行为同时保持检索效用。
URL
https://arxiv.org/abs/2512.13317
