Formation of a human iris is mainly determined by anatomical characteristics that are identity-
related and stable for personal recognition. However, existing deep Convolutional Neural …

Iris recognition in less constrained environments is challenging as the images taken therein
contain severe noisy factors. How to represent iris texture for accurate and robust …

The effectiveness of current iris recognition systems depends on the accurate segmentation
and parameterisation of the iris boundaries, as failures at this point misalign the coefficients …

Driven by the pioneer iris biometrics approach, the most relevant recognition methods
published over the years are" phase-based", and segment/normalize the iris to obtain …

In this work, we propose a robust iris segmentation method for non-ideal ocular images,
referred to as Polar Spline RANSAC, which approximates the iris shape as a closed curve …

Convolutional neural networks (CNNs) have emerged as the most popular classification
models in biometrics research. Under the discriminative paradigm of pattern recognition …

Traditional iris recognition methods are mostly based on hand-crafted features, having
limited success in less constrained scenarios due to non-ideal images caused by less …

With the rapid development of deep learning, iris recognition methods based on deep
learning are constantly being proposed. These methods generally consist of iris …

Although vision transformer has demonstrated superior performance over convolutional
networks in computer vision, its potential in iris recognition remains underexplored, primarily …

For over three decades, the Gabor-based IrisCode approach has been acknowledged as
the gold standard for iris recognition, mainly due to the high entropy and binary nature of its …