# Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning
## 1 Introduction  
The face detection system is one of the most widely used techniques for detecting human faces in digital photos and video images. Object classification in image processing aims to detect all different shapes and colours of objects present in images and classify them into the appropriate classes. Face detection, emotion classification, gender identification, and face mask recognition are examples of image processing tasks. The image processing technique describes the process of extracting valuable information from images by applying procedures to them. Importing an image, performing operations, and analysing the input image are some of the stages involved in producing the final results of the transformed images. Face detection has become an important topic due to its potential applications in biometrics, human-computer interaction (HCI), and surveillance. It is critical to supervise and monitor the use of face masks in public places. Despite advances in generic target detection algorithms in other disciplines, face mask detection techniques are currently less effective [1]. Some research has been conducted in this area by researchers who usedthe \"you only look once v2\" (YOLOv2) algorithm to build the detection model [2]. The YOLOv3 algorithm is also utilized to improve feature extraction, which is an improved spatial pyramid-like pooling structure [3]. Face mask detection also employs an algorithm for eyelid face detection and an improved self-attention mechanism of the feature pyramid network [4]. A modified YOLOv4 algorithm was developed for a tiny lightweight network technique to obtain the most relevant features of multiple targets [5]. In general, the YOLO network creates \(N\) predictions for bounding boxes for every grid with size \(G\times G\) from an image. The network cannot discover smaller items since each bounding box can only have one class during prediction. Since the ratio of the bounding box is completely learned from data, the major cause of YOLO's problem is localization. This is due to YOLO's errors with unusual ratio bounding boxes [6]. Several transfer learning approaches can also be used to solve the problem of face mask detection in the real world. As a transfer learning method, a pre-trained InceptionV3 model is used to detect people with or without masks [7]. The hybrid deep transfer learning model that combines deep learning methods with traditional computer learning is used for face mask detection [8]. The model was also created with a transfer learning concept developed on the pre-trained MobileNetV2 model for real-time face mask detection and localization [9]. Convolutional neural networks (CNN) are used in a cascading approach for detecting masked faces [10]. In terms of modern approaches, the retina face mask network is designed as a unique framework for correctly and effectively recognizing face masks [11]. Several experiments [12; 13; 14] were conducted to develop a technique that can automatically determine whether or not the person is wearing a face mask. The YOLOv3 technique and the haar cascading classifier are used to detect facial masks in real-time [15]. A comparison of four reducing deep learning approaches are used in face recognition, namely, VGGFace, FaceNet, OpenFace, and DeepFace [16]. A visual geometry group-16 (VGG-16) is also used in deep neural network (DNN) methods [17] for face mask detection. The VGG-16 architecture for real-time face mask recognition is used to create real-time facial mask identification with an alert system [18]. One of the well-researched practical issues is face detection and recognition. Significant progress has been made in facial detection technology in last few years. It is difficult because of the variations in facial structure and the existence of masks. The main goal of this research is to improve the detection performance of various masked and unmasked faces, particularly with regard to facial masks.