The key aspect of this project is to develop on a Raspberry Pi to implement a Face ID system for unlocking doors in schools and companies. It can replace the ID cards, which are always forgotten or stolen from the owner. It is based on OpenCV, which is powerful at real-time computer vision and free to use.
Raspberry Pi Zero W x1 OV5647 Mini Camera Module x1 Red LED & Green LED Resistors
Libraries need to be install for the raspi project:
sudo pip3 install opencv_python
sudo pip3 install opencv-contrib-python
sudo apt-get install libatlas-base-dev
sudo apt-get install libjasper-dev
Starting web app:
Install the required dependencies by running npm install
Start the Express server by running npm run serve.
Open the app in your browser by navigating to http://localhost:3001.
Run server
Install the necessary dependencies using npm install
Start the server using npm start
Open your web browser and go to http://localhost:3000
The premise of facial recognition is to identify the resence of a face in the image or video frame which is done with Haar Cascade Algorithm. The code for recognising faces can be devided into three parts, data gathering, train the recognizer and recognition.
Object Detection using Haar feature-based cascade classifiers is an effective object detection method. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. The algorithm does not require extensive computation and can run in real-time.
OpenCV already contains many pre-trained classifiers for face, eyes, smile, etc. Those XML files can be download from haarcascades directory.
This is a Vue web app for displaying statistics of visitor records. The app allows users to view visitor statistics over time and interact with the data by adding and deleting visitor records.
Features Visiting record statistics display: The app provides an intuitive graphical representation of visitor statistics over time, making it easy to understand the visitor trends at a glance.
Interactive visitor record management: The app allows users to add new visitor records and delete existing ones through a simple and intuitive user interface.
Secure and reliable: The app communicates with a MongoDB database to store and retrieve visitor records, ensuring the security and reliability of the data.
Feature to be added:
Camera remote streaming: WebRTC is valuable in streaming setups that require real-time latency. Peer-to-peer streaming, which is commonly called “web conferencing” or “video conferencing” is one of the top use cases of WebRTC.
This project is an Express server that is designed to process visitor records. The server has been built using Node.js and Express, and it allows users to record visitor information and store it in a database for future reference.
The server has been designed to be fast and efficient, and it can handle a large number of visitors at any given time. The data is stored in a MongoDB database, which makes it easy to manage and retrieve data as needed. The server also includes several API endpoints that allow users to retrieve data from the database and perform various operations on the visitor records. With its easy-to-use API and efficient design, it is the ideal solution for businesses and organizations that need to keep track of their visitors and their data.
This web application utilizes Flask, a Python web framework, to stream video from a camera and process data in real-time. The app allows users to access a live video feed from a camera and analyze data collected from the camera's stream. The application has a user-friendly interface with multiple features, including the ability to capture images from the video stream and process data.
dx320 Obivating
kl2420 KaiwenLiu1227
az2120 T1anyu-zhao
The project is refer to the tutorial :https://www.hackster.io/mjrobot/real-time-face-recognition-an-end-to-end-project-a10826