saba99 / Face-recognition

Face recognition app using Streamlit

Features

• Face detection and recognition
• Multi-face recognition
• Option to display recognized faces
• User-friendly interface

Requirements

• Python 3.9
• Streamlit 1.22.0
• face_recognition

Repository structure

├───dataset
│   │───ID_Name.jpg
│   │───...
├───pages
│   ├───1_🔧_Updating.py
│   └───2_💾_Database
├───Tracking.py
│───utils.py
├───config.yaml 
├───requirements.txt
├───packages.txt
└───README.md

Description

• dataset: contains images of people to be recognized. The file name format is ID_Name.jpg. For example, 1_Elon_Musk.jpg, 2_Jenna_Ortega.jpg, 3_Bill_Gates.jpg, etc. It is freely to use jpg, jpeg or png format.
• pages: contains the code for each page of the app. If you want to add more pages, you can create a new file which format is Order_Icon_Pagename in this folder, or just no-icon page with format Order_Pagename.
• Tracking.py: home page of the app, using for tracking real-time using webcam and picture.
• utils.py: contains the functions utilized by the app.
• config.yaml: contains the configuration for the app such as path of dataset dir and prompt messages.
• requirements.txt: contains the dependencies for the app.
• packages.txt: contains the packages for the app used to deploy on Streamlit Cloud.

Installation

1. Clone the repository

git clone https://github.com/datct00/Face-recognition-app-using-Streamlit.git
cd Face-recognition-app-using-Streamlit

2. Install the dependencies

pip install -r requirements.txt

3. Run the app

streamlit run Tracking.py

Usage

1. Tracking real-time using webcam
2. Tracking using a image file
3. Updating database (adding, deleting and updating)
4. Viewing the database

Demo

1. Tracking using camera

2. Tracking using picture

3. Adding new person to database

4. Deployed app on Streamlit Cloud. Click here to watch a demo of the app.