{"title":"基于人脸识别和疫苗护照验证的旋转门通道","authors":"S. Aliyeva, Ali Parsayan","doi":"10.1109/AICT55583.2022.10013579","DOIUrl":null,"url":null,"abstract":"This paper aims to provide a system ensuring turnstile access based on facial recognition and vaccine passport verification in order to enable touch-free entrance to buildings, universities, offices, etc. The algorithm of the proposed method is comprised of two essential parts: YOLO algorithm for face detection and CNN for face recognition. After successful user authentication, there are two important criteria that should be met for granting access to the person: Person should not be an active COVID-19 patient and Person should have a valid vaccine passport. The proposed method results 95.57% accuracy rate for face detection with YOLO algorithm and 70% for face recognition with CNN.","PeriodicalId":441475,"journal":{"name":"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Turnstile Access based on Facial Recognition and Vaccine Passport Verification\",\"authors\":\"S. Aliyeva, Ali Parsayan\",\"doi\":\"10.1109/AICT55583.2022.10013579\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to provide a system ensuring turnstile access based on facial recognition and vaccine passport verification in order to enable touch-free entrance to buildings, universities, offices, etc. The algorithm of the proposed method is comprised of two essential parts: YOLO algorithm for face detection and CNN for face recognition. After successful user authentication, there are two important criteria that should be met for granting access to the person: Person should not be an active COVID-19 patient and Person should have a valid vaccine passport. The proposed method results 95.57% accuracy rate for face detection with YOLO algorithm and 70% for face recognition with CNN.\",\"PeriodicalId\":441475,\"journal\":{\"name\":\"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AICT55583.2022.10013579\",\"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 16th International Conference on Application of Information and Communication Technologies (AICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AICT55583.2022.10013579","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Turnstile Access based on Facial Recognition and Vaccine Passport Verification
This paper aims to provide a system ensuring turnstile access based on facial recognition and vaccine passport verification in order to enable touch-free entrance to buildings, universities, offices, etc. The algorithm of the proposed method is comprised of two essential parts: YOLO algorithm for face detection and CNN for face recognition. After successful user authentication, there are two important criteria that should be met for granting access to the person: Person should not be an active COVID-19 patient and Person should have a valid vaccine passport. The proposed method results 95.57% accuracy rate for face detection with YOLO algorithm and 70% for face recognition with CNN.