Iris Authentication System

Iris Authentication System

Enhancing security through advanced iris recognition technology

Demo Link:
Github Repo Url: Link

The Challenge

Traditional authentication methods like passwords and PINs are prone to security breaches and user inconvenience. There was a need for a more secure and user-friendly authentication system that could leverage biometric data, specifically iris patterns, to enhance security and user experience.

The Solution

We developed an advanced iris authentication system using YOLO for eye and iris detection, combined with a Flask web application for user interaction. The system provided a highly secure and efficient method for user authentication, significantly reducing the risk of unauthorized access and improving user convenience.

Tech Mastery Showcase

PythonPython

Used for developing the core authentication logic and integrating machine learning models.

FlaskFlask

Chosen for building the web application, providing a lightweight and flexible framework for API development.

OpenCVOpenCV

Utilized for image processing tasks, including preprocessing and feature extraction from iris images.

YOLOYOLO

Leveraged for real-time eye and iris detection, ensuring accurate and efficient biometric recognition.

NumPyNumPy

Employed for numerical computations and handling large arrays of image data.

Vue.jsVue.js

Implemented for building a responsive and interactive front-end interface for user registration and authentication.

Innovative Logic & Implementation

Iris Detection and Segmentation

Developed a robust pipeline for detecting and segmenting the iris from eye images using YOLO models.

1def detect_eyes(image):
2    results = eye_model(image)
3    eyes = []
4    for r in results:
5        boxes = r.boxes
6        for box in boxes:
7            x1, y1, x2, y2 = box.xyxy[0]
8            eyes.append((int(x1), int(y1), int(x2), int(y2)))
9    return eyes

Feature Extraction and Matching

Implemented feature extraction using Gabor filters and matching algorithms to compare iris patterns for authentication.

1def extract_features(iris_mask):
2    kernel = cv2.getGaborKernel((21, 21), 8.0, np.pi / 4, 10.0, 0.5, 0, ktype=cv2.CV_32F)
3    filtered = cv2.filter2D(iris_mask, -1, kernel)
4    features = filtered.flatten() / 255.0
5    return features

Overcoming Challenges

Accurate Iris Detection

Ensuring accurate detection of the iris in various lighting conditions and eye positions was a significant challenge.

Solution:

We fine-tuned YOLO models with a diverse dataset and implemented preprocessing steps to normalize lighting and enhance contrast.

Efficient Feature Extraction

Extracting meaningful features from iris images while maintaining computational efficiency was crucial.

Solution:

We used Gabor filters for feature extraction, which provided a balance between accuracy and computational efficiency.

User-Friendly Interface

Creating an intuitive and responsive user interface for the authentication system was essential for user adoption.

Solution:

We developed a Vue.js front-end with real-time feedback and clear instructions, ensuring a seamless user experience.

Key Learnings & Growth

  • 🚀

    Gained expertise in biometric authentication systems and the challenges of iris recognition.

  • 🚀

    Enhanced skills in integrating machine learning models with web applications using Flask.

  • 🚀

    Developed proficiency in image processing techniques with OpenCV.

  • 🚀

    Improved ability to design user-friendly interfaces for complex systems.