Although Deep Neural Networks (DNNs) have achieved tremendous success in the face recognition task, utilizing them in resource-constrained environments with limited networking and computing is challenging. Such environments often demand a small model capable of being effectively trained on a small number of labeled training data, with low training complexity, and low-resolution input images. To address these challenges, we adopt an emerging machine learning methodology called Successive Subspace Learning (SSL) to propose LRFRHop, a high-performance data-efficient low-resolution face recognition model for resource-constrained environments. SSL offers an explainable non-parametric feature extraction submodel that flexibly trades the model size for the verification performance. Its training complexity is significantly lower than DNN-based models since it is trained in a one-pass feedforward manner without backpropagation. Furthermore, active learning can be conveniently incorporated to reduce the labeling cost. We demonstrate the effectiveness of LRFRHop by conducting experiments on the LFW and the CMU Multi-PIE datasets.

尽管深度神经网络 (DNN) 在人脸识别任务中取得了巨大成功，但在网络和计算有限的资源受限环境中使用它们具有挑战性。这种环境通常需要一个能够在少量标记训练数据上进行有效训练、训练复杂度低和输入图像分辨率低的小型模型。为了应对这些挑战，我们采用了一种称为连续子空间学习 (SSL) 的新兴机器学习方法来提出 LRFRHop，这是一种适用于资源受限环境的高性能数据高效低分辨率人脸识别模型。SSL 提供了一个可解释的非参数特征提取子模型，可以灵活地用模型大小来换取验证性能。它的训练复杂度明显低于基于 DNN 的模型，因为它以单遍前馈方式训练，没有反向传播。此外，可以方便地合并主动学习以降低标记成本。我们通过在 LFW 和 CMU Multi-PIE 数据集上进行实验来证明 LRFRHop 的有效性。