Iris recognition #

Identifying the characteristics of the iris is a similar process to identifying it with the facial pattern and fingerprint. The system segments the image of the iris and then converts it into a pattern which is compared with the pattern. Iris readers often use an additional system to illuminate the eye with near-infrared light.

Pre-read (Required) #

• https://www.tensorflow.org/addons/tutorials/losses_triplet
• https://www.tensorflow.org/tensorboard/tensorboard_projector_plugin
• https://www.v7labs.com/blog/image-segmentation-guide

Demo project #

This project implements image similarity (using triplet loss) for handwritten digits. The table presented as result is a list of cosine similarities for each digit (0-9) to the drawn image.

Class content #

• Introduction to the segmentation and related metrics (IoU, Dice score).
• Analysis of classic algorithms for automatic detection and segmentation of the iris (Geodesic Active Contours, SuperPixel Segmentation - SPS, Hough Transform).
• Construction of the U-NET network for iris segmentation on the CASIA V 4.0 set.
• Transformation of the iris image to the Cartesian coordinate system
• Extracting features with the Gabor filter. Analysis of the influence of light on the pattern.
• Use Hamming distance to calculate the distance between vectors
• Build iris embedding (a vector that represents the features extracted from the iris).
• Create Deep Neural Network that creates a mapping from iris images to a compact Euclidean space where distances directly represent iris similarity.