11 - Iris recognition

What

The iris is a muscle membrane of the eye, of variable color, with both shape and function of a diaphragm

• pigmented, located posterior to the cornea and in front of the lens, and is perforated by pupil.
• flat layer of muscle fibers which circularly surround the pupil, a thin layer of smooth muscle fibers by means of which the pupil is dilated (thereby regulating the amount of light that enters the eye) and posteriorly by two layers of epithelial pigmented cells
• Iris color, “regular” texture (mostly by furrows) and “irregular” patterns (e.g., freckles and crypts) provide a very high level of discrimination, which is comparable to fingerprints

Acquisition

• infrared
  • provides more accuracy

How

These operations typically requires acquisition and iris unwrapping to a 2D image. Then this image will be stored into a template db.

1. Daugman’s Integro-differential Operator:

   • How it Works: Daugman’s method analyzes the iris by looking at its unique patterns. It does this by considering local features, such as the arrangement of fibers and speckles in the iris. These features are transformed into a template, a compact representation of the iris.
   • In-Depth Explanation: Imagine the iris as a puzzle made of tiny pieces. Daugman’s method carefully examines the shapes and positions of these pieces to create a unique blueprint (template) of the iris. This blueprint is what the system uses for recognition.

2. Scale-Invariant Feature Transform (SIFT):

   • How it Works: SIFT detects distinctive local features in an image, making it robust even if the image is rotated or scaled. It finds key points in the iris pattern and creates a feature vector based on the patterns around these points.
   • In-Depth Explanation: Think of SIFT as recognizing landmarks on a mountain. No matter how you zoom in or out or change your viewpoint, these landmarks (key points) remain recognizable. SIFT captures these stable points in the iris, ensuring accurate recognition regardless of minor changes.

3. Speeded Up Robust Features (SURF):

   • How it Works: SURF is similar to SIFT but optimized for speed. It efficiently extracts features from the iris pattern, ensuring rapid processing while maintaining accuracy. It uses integral images for quick feature computation.
   • In-Depth Explanation: SURF is like a super-fast reader going through a book. Instead of carefully analyzing every word, it focuses on specific patterns (features) that stand out. By doing this efficiently, it processes the iris data quickly, making it suitable for real-time applications.

4. Gabor Filters:

   • How it Works: Gabor filters are mathematical tools that analyze texture patterns. In iris recognition, Gabor wavelets capture fine texture details in the iris, enhancing the system’s reliability.
   • In-Depth Explanation: Picture Gabor filters as tiny brushes that stroke the iris surface and feel the texture. These brushes are sensitive to different textures, capturing the fine details. By understanding these textures, Gabor filters create a detailed texture map of the iris, improving the recognition accuracy.