English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 59063/92570 (64%)
造访人次 : 733417      在线人数 : 51
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120243

    题名: Face Recognition in Intelligent Door Lock with ResNet50 Model Based on Deep Learning
    作者: Phawinee, S.;Cai, J.F.;Guo, Z.Y;Zheng, H.Z;Chen, G.C.
    关键词: Face recognition;intelligent lock;ResNet;deep learning
    日期: 2021-01-11
    上传时间: 2021-03-18 12:12:57 (UTC+8)
    摘要: Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.
    關聯: Journal of Intelligent & Fuzzy Systems, pp. 1-11
    DOI: 10.3233/JIFS-189624
    显示于类别:[機械與機電工程學系暨研究所] 期刊論文


    档案 描述 大小格式浏览次数



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈