Julie Ann B. Susa, Jonel R. Macalisang, Rovenson V. Sevilla, R. S. Evangelista, Allan Q. Quismundo, Mark P. Melegrito, R. Reyes
{"title":"基于深度学习方法的美国手语识别安全访问控制实现","authors":"Julie Ann B. Susa, Jonel R. Macalisang, Rovenson V. Sevilla, R. S. Evangelista, Allan Q. Quismundo, Mark P. Melegrito, R. Reyes","doi":"10.1109/ICETECC56662.2022.10069513","DOIUrl":null,"url":null,"abstract":"Sign language is a kind of conversation that consists of a set of gestures or postures used to converse with the deaf and mute. It is usually accomplished with hands, which implies profound signals, especially when both the receiver and sender are well-versed in the subject. Signals generated by hand gestures can also be used in a variety of applications such as augmented reality (AR), gaming, robotics, and vision-based applications. However, sign language interpretation via computer vision has yet to be implemented as a security access control, which could provide a significantly greater authentication method and better statutory provisions. The trained model’s use as a security access control system was also taken into consideration. It is done by creating a Python-based GUI that takes a single frame from a camera. A layer loss of 2.803 and an mAP of 98.69 % were the final results after 14 epochs. The study shows that when compared to earlier comparable research pursuing the same objective, this study’s validation accuracy is the highest.","PeriodicalId":364463,"journal":{"name":"2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementation of Security Access Control using American Sign Language Recognition via Deep Learning Approach\",\"authors\":\"Julie Ann B. Susa, Jonel R. Macalisang, Rovenson V. Sevilla, R. S. Evangelista, Allan Q. Quismundo, Mark P. Melegrito, R. Reyes\",\"doi\":\"10.1109/ICETECC56662.2022.10069513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sign language is a kind of conversation that consists of a set of gestures or postures used to converse with the deaf and mute. It is usually accomplished with hands, which implies profound signals, especially when both the receiver and sender are well-versed in the subject. Signals generated by hand gestures can also be used in a variety of applications such as augmented reality (AR), gaming, robotics, and vision-based applications. However, sign language interpretation via computer vision has yet to be implemented as a security access control, which could provide a significantly greater authentication method and better statutory provisions. The trained model’s use as a security access control system was also taken into consideration. It is done by creating a Python-based GUI that takes a single frame from a camera. A layer loss of 2.803 and an mAP of 98.69 % were the final results after 14 epochs. The study shows that when compared to earlier comparable research pursuing the same objective, this study’s validation accuracy is the highest.\",\"PeriodicalId\":364463,\"journal\":{\"name\":\"2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICETECC56662.2022.10069513\",\"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 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETECC56662.2022.10069513","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementation of Security Access Control using American Sign Language Recognition via Deep Learning Approach
Sign language is a kind of conversation that consists of a set of gestures or postures used to converse with the deaf and mute. It is usually accomplished with hands, which implies profound signals, especially when both the receiver and sender are well-versed in the subject. Signals generated by hand gestures can also be used in a variety of applications such as augmented reality (AR), gaming, robotics, and vision-based applications. However, sign language interpretation via computer vision has yet to be implemented as a security access control, which could provide a significantly greater authentication method and better statutory provisions. The trained model’s use as a security access control system was also taken into consideration. It is done by creating a Python-based GUI that takes a single frame from a camera. A layer loss of 2.803 and an mAP of 98.69 % were the final results after 14 epochs. The study shows that when compared to earlier comparable research pursuing the same objective, this study’s validation accuracy is the highest.