Foundation models are predominantly trained in an unsupervised or self-supervised manner on highly diverse and large-scale datasets, making them broadly applicable to various downstream tasks. In this work, we investigate for the first time whether such models are suitable for the specific domain of face recognition. We further propose and demonstrate the adaptation of these models for face recognition across different levels of data availability. Extensive experiments are conducted on multiple foundation models and datasets of varying scales for training and fine-tuning, with evaluation on a wide range of benchmarks. Our results indicate that, despite their versatility, pre-trained foundation models underperform in face recognition compared to similar architectures trained specifically for this task. However, fine-tuning foundation models yields promising results, often surpassing models trained from scratch when training data is limited. Even with access to large-scale face recognition training datasets, fine-tuned foundation models perform comparably to models trained from scratch, but with lower training computational costs and without relying on the assumption of extensive data availability. Our analysis also explores bias in face recognition, with slightly higher bias observed in some settings when using foundation models.

本研究首次探讨了基础模型是否适用于人脸识别这一特定领域。我们提出了将这些模型适应于人脸识别的方案，通过针对不同数据可用性水平进行实验，我们发现尽管基础模型在多种下游任务上表现出色，但在面对人脸识别时仍不及专门训练的模型。然而，经过微调的基础模型在有限训练数据下的表现优于从零开始训练的模型，并且在大规模数据集上也能与之媲美，同时具备更低的训练计算成本。

基础模型：基础模型是否适合人脸识别？