淡江大學機構典藏:Item 987654321/120243
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 59573/92818 (64%)
造訪人次 : 817572      線上人數 : 31
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    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


    題名: 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.
    關鍵詞: Face recognition;intelligent lock;ResNet;deep learning
    日期: 2021-01-11
    上傳時間: 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
    顯示於類別:[機械與機電工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML46檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

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