The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …

Identity management through biometrics offer potential advantages over knowledge and
possession based methods. A wide variety of biometric modalities have been tested so far …

Due to their insufficient generalization ability, iris segmentation algorithms based on deep
learning cannot accurately segment iris images without corresponding ground truth (GT) …

The volume of convolutional neural network (CNN) models proposed for face recognition
has been continuously growing larger to better fit the large amount of training data. When …

Iris recognition refers to the automated process of recognizing individuals based on their iris
patterns. The seemingly stochastic nature of the iris stroma makes it a distinctive cue for …

Multimodal biometric systems have been widely applied in many real-world applications due
to its ability to deal with a number of significant limitations of unimodal biometric systems …

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 …

This paper proposes an accurate and generalizable deep learning framework for iris
recognition. The proposed framework is based on a fully convolutional network (FCN), which …

The term “iris” refers to the highly textured annular portion of the human eye that is externally
visible. An iris recognition system exploits the richness of these textural patterns to …

The quality of iris images on mobile devices is significantly degraded due to hardware
limitations and less constrained environments. Traditional iris recognition methods cannot …