Iris Segmentation
14 papers with code • 3 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Iris Segmentation
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CASIA-Iris-Complex
UBIRIS.v2
Latest papers
Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation
dpbenalcazar/ArtificialDilation
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2022 IEEE Sixth Ecuador Technical Chapters Meeting (ETCM) 2022
The results indicate that our data augmentation method can improve segmentation accuracy up to 15% for images with high pupil dilation, which creates a more reliable iris recognition pipeline, even under extreme dilation.
Novel Deep Learning Framework For Bovine Iris Segmentation
Iris segmentation is the initial step to identify biometric of animals to establish a traceability system of livestock.
Saliency-Guided Textured Contact Lens-Aware Iris Recognition
Iris recognition requires an adequate level of the iris texture being visible to perform a reliable matching.
KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation
jalilnkh/kartalol-nir-isl2021031301
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Further, we have introduced a new dataset, called KartalOl, to better evaluate detectors in iris recognition scenarios.
Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition
In this paper, we present an end-to-end deep learning-based method for postmortem iris segmentation and recognition with a special visualization technique intended to support forensic human examiners in their efforts.
Open Source Iris Recognition Hardware and Software with Presentation Attack Detection
CVRL/RaspberryPiOpenSourceIris
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This paper proposes the first known to us open source hardware and software iris recognition system with presentation attack detection (PAD), which can be easily assembled for about 75 USD using Raspberry Pi board and a few peripherals.
Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT
iMED-Lab/WRB-Net
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We consider it to be the first work to detect angle-closure glaucoma by means of 3D representation.
Boltzmann Exploration Expectation–Maximisation
While effective, the success of any monotone algorithm is crucially dependant on good parameter initialisation, where a common choice is K-means initialisation, commonly employed for Gaussian mixture models.
A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks
To attain accurate and efficient FCN models, we propose a three-step SW/HW co-design methodology consisting of FCN architectural exploration, precision quantization, and hardware acceleration.
Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features
This paper explores the use of a Binary Statistical Features (BSIF) algorithm for classifying gender from iris texture images captured with NIR sensors.
