Face-Recognition-Access-Control

Face Recognition Access Control System based on the characteristics of students going to and coming from class.

Overview

This project is developed using Python, with a front-end developed using PySide6 and a back-end using the Flask framework. The database used is PostgreSQL.

The project file structure is as follows:

├── Client
│   ├── form.ui
│   ├── Gate.py
│   ├── main.py
│   ├── QueryRecord.py
│   ├── record.ui
│   ├── requirements.txt
│   ├── ui_form.py
│   └── ui_record.py
├── Server
|   ├── _init_paths.py
│   ├── FacenetModel.py
│   ├── ImgServer.py
│   └── requirements.txt
├── SQL
│   ├── __init__.py
│   ├── ConnectionPool.py
│   ├── Create.sql
│   ├── InsertProcessor.py
│   └── QueryProcessor.py
├── ERdiagram.excalidraw
├── LICENSE
├── README.md
└── _timeit.py

System Functionality

• Uses face recognition technology (MTCNN and FaceNet) to verify identity

• Records information about people entering and exiting doors, including the time, location, and direction of entry/exit

• Maintains a database of personnel information, including student, teacher, and staff information

• Maintains a database of face images for use in face recognition

• Maintains a log of face recognition results, recording the time and result of each recognition

Installation Instructions

Installation of this system is divided into two parts: the front-end client and the back-end server.

Client Installation

1. Download and unzip the source code for this project, and navigate to the Client directory.
2. Install dependencies: run pip install -r requirements.txt.
3. Run the client: in the Client directory, run python main.py to start the client.

Server Installation

1. Download and unzip the source code for this project, and navigate to the Server directory.
2. Install dependencies: run pip install -r requirements.txt. (ps. If you want to use CUDA to accelerate the model, you need to install the corresponding version of PyTorch and Torchvision. https://pytorch.org/get-started/locally/)
3. Install PostgreSQL and create a database with the file Create.sql.
4. Run the server: in the Server directory, run python ImgServer.py to start the server.

Database Design

This system uses a PostgreSQL database, with the following tables:

• Faculty (FacultyName, FacultyID)
• Major (MajorName, MajorID, FacultyID)
• Class (ClassID, MajorID)
• Person (ID, Name, Gender, Age, Phone, PersonType)
• Student (ID, FacultyID, MajorID, ClassID)
• Teacher (ID, FacultyID, Position)
• Worker (ID, Type)
• Record (ID, Time, DoorID, Direction, Data, Result)
• Door (DoorID, DoorLocate)
• FaceImage (ID, Data, Feature Vector)