Pre-training on large-scale datasets and utilizing margin-based loss functions have been highly successful in training models for high-resolution face recognition. However, these models struggle with low-resolution face datasets, in which the faces lack the facial attributes necessary for distinguishing different faces. Full fine-tuning on low-resolution datasets, a naive method for adapting the model, yields inferior performance due to catastrophic forgetting of pre-trained knowledge. Additionally the domain difference between high-resolution (HR) gallery images and low-resolution (LR) probe images in low resolution datasets leads to poor convergence for a single model to adapt to both gallery and probe after fine-tuning. To this end, we propose PETALface, a Parameter-Efficient Transfer Learning approach for low-resolution face recognition. Through PETALface, we attempt to solve both the aforementioned problems. (1) We solve catastrophic forgetting by leveraging the power of parameter efficient fine-tuning(PEFT). (2) We introduce two low-rank adaptation modules to the backbone, with weights adjusted based on the input image quality to account for the difference in quality for the gallery and probe images. To the best of our knowledge, PETALface is the first work leveraging the powers of PEFT for low resolution face recognition. Extensive experiments demonstrate that the proposed method outperforms full fine-tuning on low-resolution datasets while preserving performance on high-resolution and mixed-quality datasets, all while using only 0.48% of the parameters. Code: https://kartik-3004.github.io/PETALface/

翻译：在大规模数据集上进行预训练并采用基于间隔的损失函数，已成功应用于高分辨率人脸识别模型的训练。然而，这些模型在处理低分辨率人脸数据集时表现不佳，因为低分辨率人脸缺乏区分不同个体所需的面部特征。直接在低分辨率数据集上进行全参数微调是一种简单的模型适应方法，但由于会导致预训练知识的灾难性遗忘，其性能表现较差。此外，低分辨率数据集中高分辨率（HR）图库图像与低分辨率（LR）探针图像之间的域差异，使得单一模型在微调后难以同时适应图库和探针图像，导致收敛效果不佳。为此，我们提出了PETALface，一种面向低分辨率人脸识别的参数高效迁移学习方法。通过PETALface，我们尝试同时解决上述两个问题：（1）我们利用参数高效微调（PEFT）的能力来解决灾难性遗忘问题。（2）我们在骨干网络中引入两个低秩适应模块，其权重根据输入图像质量进行调整，以应对图库图像与探针图像之间的质量差异。据我们所知，PETALface是首个利用PEFT技术解决低分辨率人脸识别问题的工作。大量实验表明，所提方法在低分辨率数据集上的性能优于全参数微调，同时在高分辨率及混合质量数据集上保持了性能，且仅使用了0.48%的参数。代码：https://kartik-3004.github.io/PETALface/