Affiliations: [a] Department of Computer Science, Kasetsart University, Bangkok, Thailand
| [b]
Department of Mechanical and Electro-mechanical Engineering Tamkang University, Taiwan (ROC)
Correspondence:
[*]
Corresponding author. Guan-Chen Chen, Department of Mechanical and Electro-mechanical Engineering Tamkang University, Taiwan, ROC. E-mail: [email protected].
Abstract: 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.
Keywords: Face recognition, intelligent lock, ResNet, deep learning
