The Face Mask Detection Technology for Image Analysis in the Covid-19 Surveillance System

Abstract

Face mask recognition has been growing rapidly after corona insistent last years for its multiple uses in the areas of Law Enforcement Security purposes and other commercial uses Face appears spreading others to corona a novel approach to perform face new line detection and face mask recognition is proposed. The proposed system to classify face mask detection using COVID-19 precaution both in images and videos using convolution neural network. Extensive experimentation on the datasets and the performance evaluation of the proposed methods are exhibited. Further, we made a successful attempt to preserve inter and intra class variations of face mask detection using symbolic approach. We studied the different classifiers like Support Vector Machine and a Symbolic Classifier. The project is developed as a prototype to monitor temperature measurement and to detect mask for the people. The first method is performed using temperature sensor used to detect the present temperature of the body and automatically spray the sanitizer. In the second method, the work is designed to provide a safety system for the people in order to avoid COVID-19. We proposed continuous monitoring of the people conditions and store the people’s data in the server using the Deep learning concept. In order to investigate the performance the proposed method an extensive experimentation is conducted on 50 various Image dataset. We conducted experimentation under varying of training and testing percentage for 10 random trails. From the results we could observe that, the results obtained for symbolic approach is better than the conventional approach.

IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic

“…[44] Convolutional-neural-network-based action recognition [47][48][49][50][51] CNN adapted in the agriculture, defense, and medicine sectors. [52][53][54][55][56][57][58] Artificial Neural Networks [44,54] Layered CNN [60][61][62][63]76,77] Inceptionv3 [64,65] Super-Resolution of Images (SRCNet) [66,67] Residual Networks [68][69][70][71] Model for detecting people don't wear masks [72][73][74] Mobile Networks (MobileNet v1 and MobileNetv2) Deep learning, TensorFlow, Keras, and OpenCV [78][79][80][81] Sensors Sensor Fusion (SF) approach with MobileNetv2, deep learning [82][83][84][85][86] As shown in Table 3, CNNs are the most diffused tool for face mask and face-masked recognition detection, given the several offered advantages like spatial invariance, parameter sharing, translation invariance, and scalability [90][91]…”

“…The trained model obtained a 97% accuracy using the face mask detection algorithm. Similarly, in [86], the authors introduced a face mask detection system to fight the diffusion of the COVID-19 pandemic operating on pictures and videos; furthermore, the system could monitor body temperatures to detect potentially infected people and automatically spray the disinfectant. They explored many classifiers, including the Symbolic Classifier and Support Vector Machine (SVM).…”

Face Mask Wearing Detection using Independent Component Analysis and Naïve Bayes Classifier

“…The results obtained from the developed ICA-Naïve Bayes Classifier are compared by the results achieved using CNN, SVM, Decision Tree and using Neural Network. The accuracy of these methods is shown in Table 4 as in [28]. Using the developed methodology has a large classification improvement is observed compared to other methods in accuracy.…”

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

“…Furthermore, Teboulbi et al [ 24 ] developed a deep-learning based model for face mask detection and social distancing measurement by utilizing different CNN-based architectures. In short, several articles presented in the recent literature for face mask detection are based on CNN architectures [ 25 , 26 , 27 ]. In these articles, the authors compared the performance of two or three CNN-based architectures and proposed a model which achieved comparatively high accuracy.…”