Iris Recognition
21 papers with code • 0 benchmarks • 4 datasets
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DeepIris: Iris Recognition Using A Deep Learning Approach
Iris recognition has been an active research area during last few decades, because of its wide applications in security, from airports to homeland security border control.
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.
Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train From Scratch?
Features are extracted from each convolutional layer and the classification accuracy achieved by a Support Vector Machine is measured on a dataset that is disjoint from the samples used in training of the ResNet-50 model.
Resist : Reconstruction of irises from templates
That is, we show how to transform templates into realistic looking iris images that are also deemed as the same iris by the corresponding recognition system.
An End-to-End Autofocus Camera for Iris on the Move
To accommodate users at different distances, it is necessary to control focus quickly and accurately.
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.
KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation
Further, we have introduced a new dataset, called KartalOl, to better evaluate detectors in iris recognition scenarios.
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.
DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation
Nonlinear iris texture deformations due to pupil size variations are one of the main factors responsible for within-class variance of genuine comparison scores in iris recognition.
Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation
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.