# Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning
## 2 Related Work  
Several recent studies have been performed on face mask detection during COVID-19, and those are presented in the literature. Deep learning-based approaches were developed by researchers to study the issue of face mask detection [19]. To address the issue of masked face recognition, ResNet50 was developed as a reliable solution based on occlusion removal, as well as deep learning-based features [20]. AlexNet and VGG16 convolutional neural network designs are used as transfer learning for the development of new models [21]. Technology has been developed that prevents the spread of viruses and uses deep learning technology to ensure that people are wearing face masks correctly. The CelebA dataset was used to develop a model to automatically remove mask objects from the face and synthesize the corrupt regions while maintaining the original face structure [22]. A multi-threading strategy with VGG-16 and triplet loss FaceNet dependent on the masked faced recognition approach is proposed, which is built on MobileNet and Haar-cascade to detect face masks [23]. The embedding unmasking model (EUM) method was designed, which aimed to improve upon existing facial recognition models. The self-restrained triplet (SRT) technique was utilized, which allowed EUM to produce embeddings corresponding to the associated characters of unmasked faces [24]. The margin cosine loss (MFCosface) masked faced recognition algorithm, which is dependent on a wide margin cosine loss design, was proposed for detecting and identifying criminals. An attention-aware mechanism was improved by incorporating important facial features that helps in recognition [25]. An attention-based component using the convolutional block attention module (CBAM) model was designed, which depends on the highlighted area around the eyes [26]. The near-infrared to visible (NIR-VIS) masked faced recognition problem was analyzed in terms of the training approach and data model [27]. A method called heterogeneous semi-siamese training (HSST) was designed, which attempts to maximize the joint information between face representations using semi-siamese networks. A real-world masked face recognition dataset (RMFRD) is the largest real-world masked face dataset used for research on the face verification problem by researchers. An identity-aware mask generative adversarial network (IAMGAN) using a segmentation-guided multi-level identification module was used to produce the artificial masked face images from full face images [28]. A methodology utilized its own FaceMaskNet-21 deep learning model and also the deep metric learning approach to generate 128-dimension (128-d) face encodings [29]. This helps in face identification in static images and live video streams, as well as static video files. An attention-based approach for recognizing masked faces was proposed by combining a cropping-based strategy with a convolutional block attention module (CBAM) [30]. The attention-based model, diverse and sparse attentions-Face (DSA-Face), was developed for face recognition and face matching [31]. The DSA-Face model is composed of pairwise self-contrastive attentions (PSCA) and a sparsity loss (ASL). The PSCA was able to extract diverse local representations by enlarging pairwise attention distances, whereas the ASL shrinks responses from distracted regions in attention maps towards zero. The single shot multibox MobileNetV2 (SSDMNV2) approach uses the single shot multibox detector as a face detector, and the MobileNetV2 design serves as the classifier [32]. An end-to-end de-occlusion distillation architecture containing two modules has been developed as a methodology to transfer the mechanism of a model completion for the problem of masked face recognition [33]. The multi-granularity (MGL) model was designed for three different forms of masked face datasets for recognition, such as the simulated masked face recognition dataset (SMFRD), the masked face detection dataset (MFDD) and the real-world masked face recognition dataset (RMFRD) [34].