A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System

Abstract

Due to the global spread of the Covid-19 virus and its variants, new needs and problems have emerged during the pandemic that deeply affects our lives. Wearing masks as the most effective measure to prevent the spread and transmission of the virus has brought various security vulnerabilities. Today we are going through times when wearing a mask is part of our lives, thus it is very important to identify individuals who violate this rule. Besides, this pandemic makes the traditional biometric authentication systems less effective in many cases such as facial security checks, gated community access control, and facial attendance. So far, in the area of masked face recognition, a small number of contributions have been accomplished. It is definitely imperative to enhance the recognition performance of the traditional face recognition methods on masked faces. Existing masked face recognition approaches are mostly performed based on deep learning models that require plenty of samples. Nevertheless, currently, there are not enough image datasets that contain a masked face. As such, the main objective of this study is to identify individuals who do not use masks or use them incorrectly and to verify their identity by building a masked face dataset. On this basis, a novel real-time masked detection service and face recognition mobile application were developed based on an ensemble of fine-tuned lightweight deep Convolutional Neural Networks (CNN). The proposed model achieves 90.40% validation accuracy using 12 individuals' 1849 face samples. Experiments on the five datasets built in this research demonstrate that the proposed system notably enhances the performance of masked face recognition compared to the other state-of-the-art approaches.

Attendance System Optimization through Deep Learning Face Recognition

“…They found that the face recognition rate for identified students reached 77%, while the false-positive rate was reported at 28%. Kocacinar et al [7] developed a mobile system in realtime for recognizing masked faces using deep learning models, achieving a 90.40% validation accuracy. This system identifies full faces as well as just the eyes.…”

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

“…In [192], a DTL-FMD is introduced by fine-tuning a MobileNetv2 base model with the imageNet weights and adding a new fully-connected head to process input data for analysis. Finally, in [193], a real-time CNN-Based lightweight mobile FMD system is proposed. Three different pre-trained models, including VGG16, MobileNet, ResNet50 are fine-tuned to enhance the detection performance.…”

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

“…By utilizing a mobile application named MadFaRe [ 14 ], the authors suggested and verified a technique to discriminate between masked, unmasked, and incorrectly masked persons. Eight object detection models and four face detection models make up the Adhikarla and Davison [ 10 ] framework.…”