论文标题
低分辨率监视图像上的蒙版面部分类基准
A Masked Face Classification Benchmark on Low-Resolution Surveillance Images
论文作者
论文摘要
我们提出了一个新颖的图像数据集，专注于戴着面罩的微小面孔，以用于面罩分类，被称为“小面罩”（SF面膜），由由20K低分辨率图像制成的集合组成，这些图像由不同的和异质的数据集导出，范围从7 x 7到64 x 64 x 64 x 64 x 64 x64 x4 x64 x4 x 64 x4 x4 x4 x4 x4 x4 x4 x4 pixel。通过计数网格对该系列的准确可视化使得可以突出行人头部假定的各种姿势的差距。尤其是，没有高摄像头拍摄的面孔，其中面部特征显得很倾斜。为了解决这种结构性缺陷，我们产生了一组合成图像，从而使阶层内差异令人满意。此外，1701张图像的小子样本包含严重破坏的面罩，面向多类分类挑战。 SF面膜上的实验将重点放在使用多个分类器的面罩分类上。结果表明，在固定的1077图像测试集上，SF面膜（实际 +合成图像）的丰富度（真实 +合成图像）导致所有测试的分类器的性能要比利用比较面膜数据集更好。数据集和评估代码在此处公开可用：https：//github.com/humaticslab/sf-mask
We propose a novel image dataset focused on tiny faces wearing face masks for mask classification purposes, dubbed Small Face MASK (SF-MASK), composed of a collection made from 20k low-resolution images exported from diverse and heterogeneous datasets, ranging from 7 x 7 to 64 x 64 pixel resolution. An accurate visualization of this collection, through counting grids, made it possible to highlight gaps in the variety of poses assumed by the heads of the pedestrians. In particular, faces filmed by very high cameras, in which the facial features appear strongly skewed, are absent. To address this structural deficiency, we produced a set of synthetic images which resulted in a satisfactory covering of the intra-class variance. Furthermore, a small subsample of 1701 images contains badly worn face masks, opening to multi-class classification challenges. Experiments on SF-MASK focus on face mask classification using several classifiers. Results show that the richness of SF-MASK (real + synthetic images) leads all of the tested classifiers to perform better than exploiting comparative face mask datasets, on a fixed 1077 images testing set. Dataset and evaluation code are publicly available here: https://github.com/HumaticsLAB/sf-mask
