Backend Development 3 min read

Face Recognition Door Access Management System – Python/Django Tutorial

This article introduces a Python‑based face‑recognition door access management system built with Django, REST framework, JWT, Redis and Dlib, detailing its features, required environment setup, step‑by‑step installation commands, configuration files, and how to run the project on both PC and mobile interfaces.

Python Programming Learning Circle
Python Programming Learning Circle
Python Programming Learning Circle
Face Recognition Door Access Management System – Python/Django Tutorial

This article presents a door access management system that uses face recognition, implemented with Python and the Django framework.

The project combines Django (backend), H5/CSS/JS (frontend), MySQL (database), Redis (cache) and Dlib (face‑recognition library), and integrates JWT, SMTP, AliCloud AFS, CodePay and QQConnect for extended functionality.

Key features include dormitory access control, utility fee management, online recharge, repair requests, system logs, and more, making it suitable as a graduation design project.

Installation steps: 1) Download the source code. 2) Start MySQL (5.7.27 recommended) and Redis (default 127.0.0.1:6379, password Qq111111 ). 3) Edit settings.py to configure database connections and enable services such as SMTP, AliCloud AFS, CodePay, and QQConnect.

Database migration commands: python manage.py makemigrations python manage.py migrate

Import initial system settings from /数据库/system_setting_systemsetting.sql .

Start the application with: python manage.py runserver 127.0.0.1:8080

The article includes numerous screenshots showing the PC front‑end, mobile front‑end, and camera interface of the system.

At the end, a QR code is provided for readers to obtain additional Python learning resources for free.

backendPythonDjangoface recognitionTutorialdoor-access
Python Programming Learning Circle
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Python Programming Learning Circle

A global community of Chinese Python developers offering technical articles, columns, original video tutorials, and problem sets. Topics include web full‑stack development, web scraping, data analysis, natural language processing, image processing, machine learning, automated testing, DevOps automation, and big data.

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