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Found 25 papers, 3 papers with code
Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition
Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD).
Post-Mortem Iris Recognition Resistant to Biological Eye Decay Processes
This paper proposes an end-to-end iris recognition method designed specifically for post-mortem samples, and thus serving as a perfect application for iris biometrics in forensics.
Post-mortem Iris Decomposition and its Dynamics in Morgue Conditions
With increasing interest in employing iris biometrics as a forensic tool for identification by investigation authorities, there is a need for a thorough examination and understanding of post-mortem decomposition processes that take place within the human eyeball, especially the iris.
Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation
We propose to use deep learning-based iris segmentation models to extract highly irregular iris texture areas in post-mortem iris images.
Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples
Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation.
Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks
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-wavelet-based iris recognition.
Thermal Features for Presentation Attack Detection in Hand Biometrics
First, a PAD method operating in an open-set mode, capable of correctly discerning 100% of fake thermal samples, achieving Attack Presentation Classification Error Rate (APCER) and Bona-Fide Presentation Classification Error Rate (BPCER) equal to 0%, which can be easily implemented into any existing system as a separate component.
Iris recognition in cases of eye pathology
To make this study possible, a special database of iris images has been used, representing more than 20 different medical conditions of the ocular region (including cataract, glaucoma, rubeosis iridis, synechiae, iris defects, corneal pathologies and other) and containing almost 3000 samples collected from 230 distinct irises.
Assessment of iris recognition reliability for eyes affected by ocular pathologies
To our knowledge this is the first database of iris images for disease-affected eyes made publicly available to researchers, and the most comprehensive study of what we can expect when the iris recognition is deployed for non-healthy eyes.
Linear regression analysis of template aging in iris biometrics
The aim of this work is to determine how vulnerable different iris coding methods are in relation to biometric template aging phenomenon.
Iris and periocular recognition in arabian race horses using deep convolutional neural networks
This paper presents a study devoted to recognizing horses by means of their iris and periocular features using deep convolutional neural networks (DCNNs).
Cataract influence on iris recognition performance
Results show a significant degradation in iris recognition reliability manifesting by worsening the genuine scores in all three matchers used in this study (12% of genuine score increase for an academic matcher, up to 175% of genuine score increase obtained for an example commercial matcher).
Post-mortem Human Iris Recognition
We found that more than 90% of irises are still correctly recognized when captured a few hours after death, and that serious iris deterioration begins approximately 22 hours later, since the recognition rate drops to a range of 13. 3-73. 3% (depending on the method used) when the cornea starts to be cloudy.
Implications of Ocular Pathologies for Iris Recognition Reliability
This paper presents an analysis of how iris recognition is influenced by eye disease and an appropriate dataset comprising 2996 images of irises taken from 230 distinct eyes (including 184 affected by more than 20 different eye conditions).
Human Iris Recognition in Post-mortem Subjects: Study and Database
This paper presents a unique study of post-mortem human iris recognition and the first known to us database of near-infrared and visible-light iris images of deceased humans collected up to almost 17 days after death.
Database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease-affected eyes
This paper presents a database of iris images collected from disease affected eyes and an analysis related to the influence of ocular diseases on iris recognition reliability.
Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes
This paper presents the most comprehensive analysis of iris recognition reliability in the occurrence of various biological processes happening naturally and pathologically in the human body, including aging, illnesses, and post-mortem changes to date.
Iris Recognition with a Database of Iris Images Obtained in Visible Light Using Smartphone Camera
To our best knowledge, this is the first database of iris images captured using a mobile device, in which image quality exceeds this of a near-infrared illuminated iris images, as defined in ISO/IEC 19794-6 and 29794-6 documents.
MobiBits: Multimodal Mobile Biometric Database
This paper presents a novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device.
Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification
Results suggest that: (a) people improve their identity verification accuracy when asked to annotate matching and non-matching regions between the pair of images, (b) images depicting the same eye with large difference in pupil dilation were the most challenging to subjects, but benefited well from the annotation-driven classification, (c) humans performed better than iris recognition algorithms when verifying genuine pairs of post-mortem and disease-affected eyes (i. e., samples showing deformations that go beyond the distortions of a healthy iris due to pupil dilation), and (d) annotation does not improve accuracy of analyzing images from identical twins, which remain confusing for people.
Data-Driven Segmentation of Post-mortem Iris Images
This paper presents a method for segmenting iris images obtained from the deceased subjects, by training a deep convolutional neural network (DCNN) designed for the purpose of semantic segmentation.
Presentation Attack Detection for Cadaver Iris
We also show that the post-mortem iris detection accuracy increases as time since death elapses, and that we are able to construct a classification system with APCER=0%@BPCER=1% (Attack Presentation and Bona Fide Presentation Classification Error Rates, respectively) when only post-mortem samples collected at least 16 hours post-mortem are considered.
Perception of Image Features in Post-Mortem Iris Recognition: Humans vs Machines
This paper explores two ways of broadening this knowledge: (a) with an eye tracker, the salient features used by humans comparing iris images on a screen are extracted, and (b) class-activation maps produced by the convolutional neural network solving the iris recognition task are analyzed.
Cross-spectral Iris Recognition for Mobile Applications using High-quality Color Images
With the recent shift towards mobile computing, new challenges for biometric authentication appear on the horizon.
Iris Recognition After Death
This paper presents a comprehensive study of post-mortem human iris recognition carried out for 1, 200 near-infrared and 1, 787 visible-light samples collected from 37 deceased individuals kept in the mortuary conditions.
