论文标题
中间聚会：多尺度上的采样和匹配跨分辨率的面部识别
Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition
论文作者
论文摘要
在本文中，我们旨在解决高分辨率脸部图像之间的较大域差距，例如，从专业肖像摄影和低质量的监视图像，例如从安全摄像机来看。像这样的不同来源之间建立身份匹配是一种经典的监视面识别方案，这对于现代面部识别技术来说仍然是一个具有挑战性的问题。为此，我们提出了一种将面对超分辨率，分辨率匹配和多尺度模板积累组合在一起的方法，以可靠地识别长期监视录像中的面孔，包括低质量来源。所提出的方法不需要在实际监视图像的目标数据集上进行培训或微调。广泛的实验表明，我们提出的方法甚至能够比SCFACE数据集微调的现有方法胜过。
In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification scenario, which continues to be a challenging problem for modern face recognition techniques. To that end, we propose a method that combines face super-resolution, resolution matching, and multi-scale template accumulation to reliably recognize faces from long-range surveillance footage, including from low quality sources. The proposed approach does not require training or fine-tuning on the target dataset of real surveillance images. Extensive experiments show that our proposed method is able to outperform even existing methods fine-tuned to the SCFace dataset.
