Abstract
Recognizing and differentiating among both familiar and unfamiliar faces is a critical capability for face recognition systems and a key step toward artificial general intelligence (AGI). Motivated by this ability, this paper introduces generalized face discovery (GFD), a novel open-world face recognition task that unifies traditional face identification with generalized category discovery (GCD). GFD requires recognizing both labeled and unlabeled known identities (IDs) while simultaneously discovering new, previously unseen IDs. Unlike typical GCD settings, GFD poses unique challenges due to the high cardinality and fine-grained nature of face IDs, rendering existing GCD approaches ineffective. To tackle this problem, we propose FaceGCD, a method that dynamically constructs instance-specific feature extractors using lightweight, layer-wise prefixes. These prefixes are generated on the fly by a HyperNetwork, which adaptively outputs a set of prefix generators conditioned on each input image. This dynamic design enables FaceGCD to capture subtle identity-specific cues without relying on high-capacity static models. Extensive experiments demonstrate that FaceGCD significantly outperforms existing GCD methods and a strong face recognition baseline, ArcFace, achieving state-of-the-art results on the GFD task and advancing toward open-world face recognition.
Abstract (translated)
识别和区分熟悉与不熟悉的面孔是面部识别系统的一项关键能力，也是迈向人工通用智能（AGI）的重要一步。受到这一能力的启发，本文介绍了广义人脸发现（GFD），这是一种将传统的人脸识别与广义类别发现（GCD）相结合的新颖开放世界面部识别任务。GFD要求同时识别已标记和未标记的身份（ID），并在此过程中发现以前从未见过的新身份。与典型的GCD设置不同，由于面部ID具有高基数性和细粒度特性，因此现有的GCD方法对此类问题无效。 为了解决这一挑战，我们提出了一种名为FaceGCD的方法，该方法利用轻量级、逐层前缀动态构建实例特定的特征提取器。这些前缀由超网络实时生成，并根据每个输入图像条件化地输出一组前缀生成器。这种动态设计使FaceGCD能够在不依赖于高容量静态模型的情况下捕捉细微的身份特定线索。 广泛的实验表明，FaceGCD在现有GCD方法和强大的面部识别基线ArcFace上取得了显著的性能提升，在GFD任务中实现了最先进的结果，并为开放世界的面部识别技术进步作出了贡献。
URL
https://arxiv.org/abs/2507.22353
