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Masked face recognition Using MobileNet V2 with Transfer Learning

Computer systems Science & Engineering 2023年第4期45卷 293-309页

作者： Ratnesh Kumar Shukla Arvind Kumar Tiwari Department of Computer Science&Engineering Dr.APJ Abdul Kalam Technical UniversityLucknow226021India Department of Computer Science&Engineering KNITSultanpur228118Uttar PradeshIndia

Corona virus(COVID-19)is once in a life time calamity that has resulted in thousands of deaths and security *** are using face masks on a regular basis to protect themselves and to help reduce corona virus *** the on-going coronavirus outbreak,one of the major priorities for researchers is to discover effective *** important parts of the face are obscured,face identification and verification becomes exceedingly *** suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model,to identify the problem of face masked *** the first stage,we are applying face mask detector to identify the face ***,the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10(CIFAR10),Modified National Institute of Standards and Technology Database(MNIST),Real World Masked face recognition Database(RMFRD),and Stimulated Masked face recognition Database(SMFRD).The proposed model is achieving recognition accuracy 99.82%with proposed *** article employs the four pre-programmed models VGG16,VGG19,ResNet50 and *** extract the deep features of faces with VGG16 is achieving 99.30%accuracy,VGG19 is achieving 99.54%accuracy,ResNet50 is achieving 78.70%accuracy and ResNet101 is achieving 98.64%accuracy with own *** comparative analysis shows,that our proposed model performs better result in all four previous existing *** fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.

关键词： Convolutional Neural Network(CNN) deep learning face recognition system COVID-19 dataset and machine learning based models

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