6 research outputs found
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. We used four
different iris recognition methods to analyze the dynamics of iris quality
decay in short-term comparisons (samples collected up to 60 hours after death)
and long-term comparisons (for samples acquired up to 407 hours after demise).
This study shows that post-mortem iris recognition is possible and occasionally
works even 17 days after death. These conclusions contradict a promulgated
rumor that iris is unusable shortly after decease. We make this dataset
publicly available to let others verify our findings and to research new
aspects of this important and unfamiliar topic. We are not aware of any earlier
papers offering post-mortem human iris images and such comprehensive analysis
employing four different matchers.Comment: Accepted manuscript version of the BTAS2016 pape
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. Insightful conclusions are offered in relation to
all three of these aspects. Extensive regression analysis of the template aging
phenomenon shows that differences in pupil dilation, combined with certain
quality factors of the sample image and the progression of time itself can
significantly degrade recognition accuracy. Impactful effects can also be
observed when iris recognition is employed with eyes affected by certain eye
pathologies or (even more) with eyes of the deceased subjects. Notably,
appropriate databases are delivered to the biometric community to stimulate
further research in these utterly important areas of iris biometrics studies.
Finally, some open questions are stated to inspire further discussions and
research on these important topics. To Authors' best knowledge, this is the
only scientific study of iris recognition reliability of such a broad scope and
novelty.Comment: Accepted manuscript version of the BIOSIGNALS 2017 pape
Post-mortem Human Iris Recognition
This paper presents a unique analysis of post-mortem human iris recognition.
Post-mortem human iris images were collected at the university mortuary in
three sessions separated by approximately 11 hours, with the first session
organized from 5 to 7 hours after demise. Analysis performed for four
independent iris recognition methods shows that the common claim of the iris
being useless for biometric identification soon after death is not entirely
true. Since the pupil has a constant and neutral dilation after death (the so
called "cadaveric position"), this makes the iris pattern perfectly visible
from the standpoint of dilation. 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. There were only two failures to enroll
(out of 104 images) observed for only a single method (out of four employed in
this study). These findings show that the dynamics of post-mortem changes to
the iris that are important for biometric identification are much more moderate
than previously believed. To the best of our knowledge, this paper presents the
first experimental study of how iris recognition works after death, and we hope
that these preliminary findings will stimulate further research in this area.Comment: Accepted for publication version of the manuscript submitted for the
IEEE ICB 201
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. This can prove useful for
fast and accurate matching of ante-mortem with post-mortem data acquired at
crime scenes or mass casualties, as well as for ensuring correct dispatching of
bodies from the incident scene to a mortuary or funeral homes. Following these
needs of forensic community, this paper offers an analysis of the coarse
effects of eyeball decay done from a perspective of automatic iris recognition
point of view. Therefore, we analyze post-mortem iris images acquired in both
visible light as well as in near-infrared light (860 nm), as the latter
wavelength is used in commercial iris recognition systems. Conclusions and
suggestions are provided that may aid forensic examiners in successfully
utilizing iris patterns in post-mortem identification of deceased subjects.
Initial guidelines regarding the imaging process, types of illumination,
resolution are also given, together with expectations with respect to the iris
features decomposition rates
Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation
This paper proposes the first known to us iris recognition methodology
designed specifically for post-mortem samples. We propose to use deep
learning-based iris segmentation models to extract highly irregular iris
texture areas in post-mortem iris images. We show how to use segmentation masks
predicted by neural networks in conventional, Gabor-based iris recognition
method, which employs circular approximations of the pupillary and limbic iris
boundaries. As a whole, this method allows for a significant improvement in
post-mortem iris recognition accuracy over the methods designed only for
ante-mortem irises, including the academic OSIRIS and commercial IriCore
implementations. The proposed method reaches the EER less than 1% for samples
collected up to 10 hours after death, when compared to 16.89% and 5.37% of EER
observed for OSIRIS and IriCore, respectively. For samples collected up to 369
hours post-mortem, the proposed method achieves the EER 21.45%, while 33.59%
and 25.38% are observed for OSIRIS and IriCore, respectively. Additionally, the
method is tested on a database of iris images collected from ophthalmology
clinic patients, for which it also offers an advantage over the two other
algorithms. This work is the first step towards post-mortem-specific iris
recognition, which increases the chances of identification of deceased subjects
in forensic investigations. The new database of post-mortem iris images
acquired from 42 subjects, as well as the deep learning-based segmentation
models are made available along with the paper, to ensure all the results
presented in this manuscript are reproducible.Comment: Paper submitted for the Elsevier Image and Vision Computing Journal
on Jan 5th, 2019, revised versio
Iris recognition in cases of eye pathology
This chapter provides insight on how iris recognition, one of the leading
biometric identification technologies in the world, can be impacted by
pathologies and illnesses present in the eye, what are the possible
repercussions of this influence, and what are the possible means for taking
such effects into account when matching iris samples.
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. Then, with the use of four different iris recognition
methods, a series of experiments has been conducted, concluding in several
important observations.
One of the most popular ocular disorders worldwide - the cataract - is shown
to worsen genuine comparison scores when results obtained from
cataract-affected eyes are compared to those coming from healthy irises. An
analysis devoted to different types of impact on eye structures caused by
diseases is also carried out with significant results. The enrollment process
is highly sensitive to those eye conditions that make the iris obstructed or
introduce geometrical distortions. Disorders affecting iris geometry, or
producing obstructions are exceptionally capable of degrading the genuine
comparison scores, so that the performance of the entire biometric system can
be influenced. Experiments also reveal that imperfect execution of the image
segmentation stage is the most prominent contributor to recognition errors.Comment: Accepted for publication as a chapter in A. Nait-Ali (Ed.),
"Biometrics under Biomedical Considerations", Springer, 2019, ISBN
978-981-13-1143-