Deep Learning Approach for Masked Face Identification

Abstract

Covid-19 is a global health emergency and a major concern in the industrial and residential sectors. It has the ability to spread leading to health problems or death. Wearing a mask in public locations and busy areas is the most effective COVID-19 prevention measure. Face recognition provides an accurate method that overcomes uncertainties such as false prediction, high cost, and time consumption, as it is understood that the primary identification for every human being is his face. As a result, masked face identification is required to solve the issue of recognizing individuals with masks in several applications such as door access systems and smart attendance systems. This paper offers an important and intelligent method to solve this issue. We propose deep transfer learning approach for masked face human identification. We created a dataset of masked-face images and examined six convolutional neural network (CNN) models on this dataset. All models show great performance in terms of very high face recognition accuracy and short training time.

Deep Transfer Learning Approach for Student Attendance System During the COVID-19 Pandemic

“…Employed pre-trained models, including squeeze net, google net, AlexNet, ResNet50, VGG-16, and MobileNetV2, for masked face human identification. Remarkably, these six models demonstrated validation accuracy ranging from 97.8-100% when tested on datasets comprising 400 RGB images (Shatnawi et al, 2022). Furthermore, a mask classification model was developed using the mobilenetV2 framework through deep transfer learning, achieving an accuracy of 97.01% on the validation data, 98% on the training data, and 97.45% on the testing data.…”

Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier

“…They discuss the importance of developing robust detection methods to prevent unauthorized access and ensure the integrity of face recognition systems. Shatnawi et al [ 37 ] propose a deep learning approach for masked face identification where they use deep convolution neural network (DCNN)- and MobileNetV2-based transfer learning models to detect face masks in public places to curtail the spread of Coronavirus. The models are trained, validated, and tested on different data sets, achieving high accuracy.…”