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
翻译:我们提议建立一个新的图像数据集,侧重于面罩面罩的小面孔,用于面罩分类目的,称为SF-MASK(SF-MASK),由从多样化和多样化的数据集中输出的20公里低分辨率图像组成,范围从7x7至64x64等像素分辨率不等,从7x7至64等离子体分辨率到64等离子体分辨率不等的20公里低清晰度图像组成。通过计算网格,精确地直观地展示了这一收藏,从而有可能突出行人头所承受的各种面罩的差别。特别是,用非常高的照相机拍摄的面部特征明显扭曲的面部面部面部没有。为了解决这一结构性缺陷,我们制作了一套合成图像,从而令人满意地覆盖了各阶层内部差异。此外,一个小型的1701层图像的子样本包含破损面罩,打开了多级分类的挑战。对SF-MASK在面罩分类上以几个分层仪进行重点的实验,结果显示,SF-MASK(真实+合成图像)的丰富性使所有经过测试的分类者都比利用面罩数据集要好,而不是利用1077/Hmatub/公开的面罩式图像测试。