Face Recognition Based on MTCNN and MobileFaceNet
Your project has designed a deep learning-based face recognition system with a front-end and back-end separated implementation. This system includes a front-end page and a back-end service, which can be used for face registration and real-time face recognition. Below are detailed analysis and improvement suggestions for your code: ### Front-end Part 1. **HTML Template**: - You have already created a simple `index.html` file in the `templates` directory to provide the user interface. - Some basic CSS styles can be added.
Read MoreFace Landmark Detection Model MTCNN Implemented with PaddlePaddle
The article introduces the process of using MTCNN (Multi-Task Convolutional Neural Network) for face detection, which includes three hierarchical networks: P-Net, R-Net, and O-Net. P-Net is used to generate candidate windows, R-Net performs precise selection and regresses bounding boxes and key points, while O-Net further refines the output to get the final bounding box and key point locations. The project source code is hosted on GitHub and implemented using PaddlePaddle 2.0.1. The model training consists of three steps: first, training the PNet to generate candidate windows; then, using PNet data to train the RNet for... (Note: The original Chinese text appears to be truncated at this point; the translation continues as per the provided content.)
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