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Iris Segmentation

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Showing 1–25 of 37 papers

TitleStatusHype
Post-mortem Iris Recognition with Deep-Learning-based Image SegmentationCode1
Artificial Pupil Dilation for Data Augmentation in Iris Semantic SegmentationCode1
Iris-SAM: Iris Segmentation Using a Foundation ModelCode1
Open Source Iris Recognition Hardware and Software with Presentation Attack DetectionCode1
Data-Driven Segmentation of Post-mortem Iris Images—0
DeepIrisNet2: Learning Deep-IrisCodes from Scratch for Segmentation-Robust Visible Wavelength and Near Infrared Iris Recognition—0
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets—0
Direct attacks using fake images in iris verification—0
Experimental analysis regarding the influence of iris segmentation on the recognition rate—0
Influence of segmentation on deep iris recognition performance—0
Unconstrained Iris Segmentation using Convolutional Neural Networks—0
An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model—0
An approach to human iris recognition using quantitative analysis of image features and machine learning—0
An End to End Deep Neural Network for Iris Segmentation in Unconstraint Scenarios—0
Iris segmentation techniques to recognize the behavior of a vigilant driver—0
Micro Stripes Analyses for Iris Presentation Attack Detection—0
Normalized Validity Scores for DNNs in Regression based Eye Feature Extraction—0
Post-Mortem Human Iris Segmentation Analysis with Deep Learning—0
Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks—0
SegDenseNet: Iris Segmentation for Pre and Post Cataract Surgery—0
Segmentation-free Direct Iris Localization Networks—0
Simultaneous Iris and Periocular Region Detection Using Coarse Annotations—0
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments—0
Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning—0
A fast and accurate iris segmentation method using an LoG filter and its zero-crossings—0
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