Face mask detection and masked facial recognition dataset (MDMFR Dataset)

The unavailability of a unified standard dataset for face mask detection and masked facial recognition motivated us to develop an in-house MDMFR dataset (MDMFR, 2022) to measure the performance of face mask detection and masked facial recognition methods. Both of these tasks have different dataset requirements. Face mask detection requires the images of multiple persons with and without mask. Whereas, masked face recognition requires multiple masked face images of the same person. Our MDMFR dataset consists of two main collections, 1) face mask detection, and 2) masked facial recognition. There are 6006 images in our MDMFR dataset. The face mask detection collection contains two categories of face images i.e., mask and unmask. Our detection database consists of 3174 with mask and 2832 without mask (unmasked) images. To construct the dataset, we captured multiple images of the same person in two configurations (mask and without mask). The masked facial recognition collection contains a total of 2896 masked images of 226 persons. More specifically, our dataset includes the images of both male and female persons of all ages including the children. The images of our dataset are diverse in terms of gender, race, and age of users, types of masks, illumination conditions, face angles, occlusions, environment, format, dimensions, and size, etc. Before being fed to our DeepMaskNet model, all images are scaled to a width and height of 256 pixels. All images have a bit depth of 24. We prepared the images of our dataset for the proposed DeepMaskNet model during preprocessing where images are cropped in Adobe-Photoshop to exclude the extra information like neck and shoulder. As the input size of our Deepmasknet model was 256-by-256, so images were resized to 256-by-256 in publicly available Plastiliq Image Resizer software (Plastiliq, 2022).

鉴于口罩检测与带口罩人脸识别领域尚无统一标准数据集，我们自研了MDMFR数据集（MDMFR，2022），用于测评口罩检测及带口罩人脸识别方法的性能。两类任务对数据集的需求存在显著差异：口罩检测任务需涵盖佩戴口罩与未佩戴口罩的多人面部图像；而带口罩人脸识别任务则需要同一身份主体的多张带口罩面部图像。本MDMFR数据集包含两大核心子集：1）口罩检测子集，2）带口罩人脸识别子集，总计包含6006张图像。其中口罩检测子集包含佩戴口罩、未佩戴口罩两类面部图像；该数据库中共包含3174张佩戴口罩的图像与2832张未佩戴口罩的图像。为构建该数据集，我们针对同一主体拍摄了佩戴口罩、未佩戴口罩两种配置下的多张图像。带口罩人脸识别子集总计包含226位主体的2896张带口罩面部图像。具体而言，本数据集涵盖了各年龄段的男性与女性主体，包括儿童群体；图像在主体性别、种族、年龄、口罩类型、光照条件、面部角度、遮挡情况、拍摄环境、图像格式、分辨率维度与尺寸等多个维度均具备丰富多样性。在将图像输入DeepMaskNet模型之前，所有图像均被缩放至宽高均为256像素，且所有图像的位深度均为24。我们在预处理阶段为所提出的DeepMaskNet模型完成了数据集图像的适配工作：使用Adobe Photoshop对图像进行裁剪，以剔除颈部、肩部等多余背景信息。由于DeepMaskNet模型的输入尺寸为256×256像素，因此我们通过公开可用的Plastiliq图像调整软件（Plastiliq，2022）将所有图像统一调整至该尺寸。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于口罩检测和口罩面部识别的综合性数据集，包含6006张图像，分为口罩检测（3174张戴口罩和2832张不戴口罩图像）和口罩面部识别（226人的2896张戴口罩图像）两部分。图像具有多样性，覆盖不同性别、种族、年龄、口罩类型和拍摄条件，并经过预处理以适应深度学习模型。