Iris images captured in non-cooperative environments often suffer from adverse noise, which
challenges many existing iris segmentation methods. To address this problem, this paper …

Iris recognition is a secure biometric technology known for its stability and privacy. With no
two irises being identical and little change throughout a person's lifetime, iris recognition is …

Iris is one of the most widely used biometric modalities because of its uniqueness, high
matching performance, and inherently secure nature. Iris segmentation is an essential …

Image segmentation is a critical task in the field of computer vision. In the past, traditional
segmentation algorithms were frequently used to tackle this problem but had various …

In recent years, cross-spectral iris recognition has emerged as a promising biometric
approach to establish the identity of individuals. However, matching iris images acquired at …

Multiple ocular region segmentation plays an important role in different applications such as
biometrics, liveness detection, healthcare, and gaze estimation. Typically, segmentation …

Recently, ocular biometrics in unconstrained environments using images obtained at visible
wavelength have gained the researchers' attention, especially with images captured by …

This paper offers three new, open-source, deep learning-based iris segmentation methods,
and the methodology how to use irregular segmentation masks in a conventional Gabor …

One of the major challenges in ocular biometrics is the cross‐spectral scenario, ie how to
match images acquired in different wavelengths. This study designs and extensively …

The paper presents an efficient lightweight U-net convolutional neural network (CNN)
architecture that can be used for iris segmentation in eye images. The novelty of the …