论文标题
资源约束环境中的低分辨率面部识别
Low-Resolution Face Recognition In Resource-Constrained Environments
论文作者
论文摘要
这项工作提出了针对资源约束环境有限的资源约束环境的非参数低分辨率面部识别模型。这样的环境通常需要一个小型模型，能够在少数标记的数据样本上有效训练，训练复杂性低和低分辨率输入图像。为了应对这些挑战，我们采用了一种可解释的机器学习方法，称为连续的子空间学习（SSL）.SSL提供了一个可解释的非参数模型，该模型灵活地交易了模型大小以进行验证性能。它的训练复杂性大大降低，因为它的模型以一种通用的饲料方式训练而没有反向传播。此外，可以方便地进行积极学习以降低标签成本。 LFW和CMU多PIE数据集的实验证明了所提出的模型的有效性。
A non-parametric low-resolution face recognition model for resource-constrained environments with limited networking and computing is proposed in this work. Such environments often demand a small model capable of being effectively trained on a small number of labeled data samples, with low training complexity, and low-resolution input images. To address these challenges, we adopt an emerging explainable machine learning methodology called successive subspace learning (SSL).SSL offers an explainable non-parametric model that flexibly trades the model size for verification performance. Its training complexity is significantly lower since its model is trained in a one-pass feedforward manner without backpropagation. Furthermore, active learning can be conveniently incorporated to reduce the labeling cost. The effectiveness of the proposed model is demonstrated by experiments on the LFW and the CMU Multi-PIE datasets.
