{"title":"Deep learning based face recognition system with smart glasses","authors":"O. Daescu, Hongyao Huang, Maxwell Weinzierl","doi":"10.1145/3316782.3316795","DOIUrl":null,"url":null,"abstract":"Individuals with prosopagnosia have difficulty in identifying different people by their faces. Our goal is to design and develop a face recognition system with wearable glasses to recognize faces and provide identity information to users. Unlike other existing systems that run locally on glasses or cellphones, we introduce a client-server architecture system for facial identification. We designed and implemented applications both on a pair of smart glasses and a cellphone to capture images and communicate with the server. Deep Convolutional Neural Networks (CNN) were chosen to build our face recognition on the back-end system and we achieved 98.18% accuracy for face recognition. The system is designed to handle new identities and new faces without having to rebuild the model.","PeriodicalId":264425,"journal":{"name":"Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3316782.3316795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Individuals with prosopagnosia have difficulty in identifying different people by their faces. Our goal is to design and develop a face recognition system with wearable glasses to recognize faces and provide identity information to users. Unlike other existing systems that run locally on glasses or cellphones, we introduce a client-server architecture system for facial identification. We designed and implemented applications both on a pair of smart glasses and a cellphone to capture images and communicate with the server. Deep Convolutional Neural Networks (CNN) were chosen to build our face recognition on the back-end system and we achieved 98.18% accuracy for face recognition. The system is designed to handle new identities and new faces without having to rebuild the model.