# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 4 Evaluation Methodology
### First Stage--Face Detection  
**Detection models. ** Face detection has made significant progress over the last decade, mainly due to advances in deep-learning and convolutional neural networks (CNNs). As a result, most of the existing state-of-the-art (SOTA) face-detection techniques are CNN-based and are today able to efficiently detect faces with challenging characteristics and variability across pose, scale, illumination, from poor-quality data and in the presence of various other nuisance factors. Although face detection has been studied for faces with various occlusions, not much research has been done on investigating detection performance with masked faces specifically. In this paper, we explore this issue using the following (pretrained) models:  
* Multi-Task Cascaded Convolutional Neural Network (MTCNN) [36]--MTCNN represents a carefully designed cascaded CNN-based framework for joint face detection and alignment. The detection process of MTCNN consists of three stages of convolutional networks that can predict face and landmark locations in a coarse-to-fine manner. A publicly available version of MTCNN is used for the experiments ([[https://github.com/matlab-deep-learning/mtcnn-face-detection/releases/tag/v1.2.3](https://github.com/matlab-deep-learning/mtcnn-face-detection/releases/tag/v1.2.3)]([https://github.com/matlab-deep-learning/mtcnn-face-detection/releases/tag/v1.2.3](https://github.com/matlab-deep-learning/mtcnn-face-detection/releases/tag/v1.2.3)), accessed on 25 February 2021). * The OpenCV single-shot detector (OCVSSD)--OCVSSD is a Single-Shot Detector (SSD) framework with a ResNet base network [37]. The detector is Caffe-based and has been part of OpenCV since OpenCV 3.3 ([[https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector)]([https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector)), accessed on 25 February 2021). * The Dual Shot Face Detector (DSFD) [38]--The DSFD is an extension of the standard single-shot face detector (SSD) that improves upon SSD in terms of more descriptive feature maps, a more efficient learning objective (loss) and an improved strategy of matching predictions to the faces in the input images. We use improved Pytorch imple  
Figure 4: Overall pipeline designed for the evaluation procedure. A two-stage pipeline consisting of a face detection and a face classification stage is used. Unlike existing studies, we are not interested in whether face-masks are present in the images or not, but whether they are worn in a recommendation compliant manner. mentation of the original algorithm ([[https://github.com/hukkelas/DSFD-Pytorch-Inference](https://github.com/hukkelas/DSFD-Pytorch-Inference)]([https://github.com/hukkelas/DSFD-Pytorch-Inference](https://github.com/hukkelas/DSFD-Pytorch-Inference)), accessed on accessed on 25 February 2021). * RetinaFace [29]--RetinaFace is a single-shot multi-level face localization method that performs pixel-wise face localization on various scales by taking advantage of multi-task learning. It jointly performs three different face localization tasks, i.e., face detection, 2D face alignment and 3D face reconstruction within a single framework ([[https://github.com/biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface)]([https://github.com/biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface)), accessed on 25 February 2021). * BAIDU detector--The Baidu detector is based on PyramidBox: A context-assisted single-shot face detector [35] that is used as part of Baidu's face-mask detector. PyramidBox implements multiple strategies to use context information to improve the face-detection results. * AntiCov--The RetinaFace AntiCov is a customized one-stage face detector based on RetinaFace [29].