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

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

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

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.

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.

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)