This project aims to develop a biometric system for individual identification using iris recognition. The system allows users to input an iris image through a graphical interface and display the identity of the identified person from the database after processing steps.
- Extracts iris images from the Iris Database (upol.cz), consisting of 3 x 128 iris images (i.e., 3 x 64 left and 3 x 64 right).
- Utilizes the Scale Invariant Feature Transform (SIFT) method for feature extraction.
- Matches iris images using Euclidean distance.
- Provides a graphical user interface for user interaction.
- Python 3.x installed
- Pip package manager installed
1. Install "Git" :
Windows :
https://github.com/git-for-windows/git/releases/download/v2.45.0.windows.1/Git-2.45.0-64-bit.exe
Linux (Debian/Ubuntu) :
apt-get install git
2. Clone the repository:
git clone https://github.com/OUARAS-khelil-Rafik/iris_biometric_system.git
3. Navigate to the project directory:
cd iris_biometric_system
4. Install dependencies:
pip install -r requirements.txt
5. Run the application:
python src/main.py
- Launch the application using python src/main.py.
- Upload an iris image using the "Upload Iris" button.
- Follow the on-screen instructions to view the matching result.
Khelil Rafik OUARAS (@OUARAS-khelil-Rafik)
- pip=24.0
- python=3.12.2
- sqlite3=3.43.1
- tkinter=8.6
- PIL=10.2.0
- numpy==1.26.3
- opencv-python==4.9.0.80
- imageio==2.34.0
- Pillow==10.3.0
- tk==0.1.0
- matplotlib==3.8.3
.png)
The verification process involves using BFMatcher "Distance euclidienne" image matching library. The image of the user's IRIS captured live during the verification screening is fed through the method match_images(), along with the registered captured IRIS with the ID the user inputted. If there is a match using BFMatcher "brute force matching", the user input information will be displayed in a dialog box (new frame). The match_rate conditional statement will have to be adjusted based on what video capture system is used. For stronger cameras, a match rate greater then 50% will work. For weaker camera devices such as a wireless webcam, a lower value such as 35% should be considered.
.png)
.png)