{"title":"卷积神经网络合成遮挡人脸识别","authors":"I. Recto, M. Devaraj","doi":"10.1109/IAICT55358.2022.9887517","DOIUrl":null,"url":null,"abstract":"Wearing a face mask is the norm during the COVID–19 pandemic and is advised for enclosed spaces such as workplaces. In face recognition, a face mask is considered a partial occlusion which degrades recognition accuracy. This study focuses on the occlusion factor by a variety of face mask designs. This study aims to mitigate the impact of face masks as an occlusion on a face recognition system. We superimposed a synthetic face mask and black occlusions on top of the face images (FI). FaceNet, a deep convolutional neural network, was used to extract facial embeddings. The faces were classified using a support vector machine. We experimented with different scenarios by using different training sets and testing sets, contains differing mask designs. It achieved a performance of recognizing occluded lower FI with an average accuracy rate of 98.93% in a controlled environment.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Synthetic Occluded Masked Face Recognition using Convolutional Neural Networks\",\"authors\":\"I. Recto, M. Devaraj\",\"doi\":\"10.1109/IAICT55358.2022.9887517\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wearing a face mask is the norm during the COVID–19 pandemic and is advised for enclosed spaces such as workplaces. In face recognition, a face mask is considered a partial occlusion which degrades recognition accuracy. This study focuses on the occlusion factor by a variety of face mask designs. This study aims to mitigate the impact of face masks as an occlusion on a face recognition system. We superimposed a synthetic face mask and black occlusions on top of the face images (FI). FaceNet, a deep convolutional neural network, was used to extract facial embeddings. The faces were classified using a support vector machine. We experimented with different scenarios by using different training sets and testing sets, contains differing mask designs. It achieved a performance of recognizing occluded lower FI with an average accuracy rate of 98.93% in a controlled environment.\",\"PeriodicalId\":154027,\"journal\":{\"name\":\"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAICT55358.2022.9887517\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAICT55358.2022.9887517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Synthetic Occluded Masked Face Recognition using Convolutional Neural Networks
Wearing a face mask is the norm during the COVID–19 pandemic and is advised for enclosed spaces such as workplaces. In face recognition, a face mask is considered a partial occlusion which degrades recognition accuracy. This study focuses on the occlusion factor by a variety of face mask designs. This study aims to mitigate the impact of face masks as an occlusion on a face recognition system. We superimposed a synthetic face mask and black occlusions on top of the face images (FI). FaceNet, a deep convolutional neural network, was used to extract facial embeddings. The faces were classified using a support vector machine. We experimented with different scenarios by using different training sets and testing sets, contains differing mask designs. It achieved a performance of recognizing occluded lower FI with an average accuracy rate of 98.93% in a controlled environment.