A. Sameera, V. Samuktha, T. Akash, M. Sabeshnav, S. Veni
{"title":"使用深度学习实时检测各种衰老迹象","authors":"A. Sameera, V. Samuktha, T. Akash, M. Sabeshnav, S. Veni","doi":"10.1109/wispnet54241.2022.9767121","DOIUrl":null,"url":null,"abstract":"One of the most promising fields where the technology of deep learning and CNN can thrive are the cosmetic and dermatology industries. Detection of conditions like premature ageing can be made easy by deep learning procedures like facial detection and recognition. This project is based on improving the technology principally in these domains. A deep learning model utilizing CNNs is built, and the network is equipped with hand-crafted characteristics like wrinkles, acne and blemishes. The model will be able to distinguish these features concurrently and has diverse applications. It is computationally efficient compared to previous models, and it uses special convolution and pooling operations and performs parameter shifting. An overall accuracy of 94.11 % was achieved.","PeriodicalId":432794,"journal":{"name":"2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Real Time Detection of the Various Sign of Ageing Using Deep Learning\",\"authors\":\"A. Sameera, V. Samuktha, T. Akash, M. Sabeshnav, S. Veni\",\"doi\":\"10.1109/wispnet54241.2022.9767121\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the most promising fields where the technology of deep learning and CNN can thrive are the cosmetic and dermatology industries. Detection of conditions like premature ageing can be made easy by deep learning procedures like facial detection and recognition. This project is based on improving the technology principally in these domains. A deep learning model utilizing CNNs is built, and the network is equipped with hand-crafted characteristics like wrinkles, acne and blemishes. The model will be able to distinguish these features concurrently and has diverse applications. It is computationally efficient compared to previous models, and it uses special convolution and pooling operations and performs parameter shifting. An overall accuracy of 94.11 % was achieved.\",\"PeriodicalId\":432794,\"journal\":{\"name\":\"2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/wispnet54241.2022.9767121\",\"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 Wireless Communications Signal Processing and Networking (WiSPNET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/wispnet54241.2022.9767121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real Time Detection of the Various Sign of Ageing Using Deep Learning
One of the most promising fields where the technology of deep learning and CNN can thrive are the cosmetic and dermatology industries. Detection of conditions like premature ageing can be made easy by deep learning procedures like facial detection and recognition. This project is based on improving the technology principally in these domains. A deep learning model utilizing CNNs is built, and the network is equipped with hand-crafted characteristics like wrinkles, acne and blemishes. The model will be able to distinguish these features concurrently and has diverse applications. It is computationally efficient compared to previous models, and it uses special convolution and pooling operations and performs parameter shifting. An overall accuracy of 94.11 % was achieved.