Reliability of Automatic Biometric Iris Recognition after Phacoemulsification or Drug-Induced Pupil Dilation

Biometrics Under Biomedical Considerations

2018

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.

Section: Glaucomamentioning

confidence: 99%

Iris Recognition in Cases of Eye Pathology

Biometrics Under Biomedical Considerations

2018

“…A similar scenario regarding the impact of cataract surgery was explored by Seyeddain et al [14] who performed an experiment to establish the effects of phacoemulsification and pharmacologically induced mydriasis on the iris. (Phacoemulsification refers to the extraction of the lens through aspiration.…”

Section: Related Workmentioning

confidence: 99%

“…Researchers carried out an experiment to find out whether such manipulation may result in increased FNMR of an iris biometric system. Using Maseks algorithm for encoding, 13 eyes (out of 14) were correctly recognized after a procedure had been performed. However, the one eye that was falsely non-matched had a significant deviation in circularity of the pupil and increased pupil diameter.…”

Section: Related Workmentioning

confidence: 99%

Implications of ocular pathologies for iris recognition reliability

Image and Vision Computing

2017

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). The images were collected in near infrared and visible light during routine ophthalmological examination. The experimental study carried out utilizing four independent iris recognition algorithms (MIRLIN, VeriEye, OSIRIS and IriCore) renders four valuable results. First, the enrollment process is highly sensitive to those eye conditions that obstruct the iris or cause geometrical distortions. Second, even those conditions that do not produce visible changes to the structure of the iris may increase the dissimilarity between samples of the same eyes. Third, eye conditions affecting the geometry or the tissue structure of the iris or otherwise producing obstructions significantly decrease same-eye similarity and have a lower, yet still statistically significant, influence on impostor comparison scores. Fourth, for unhealthy eyes, the most prominent effect of disease on iris recognition is to cause segmentation errors. To our knowledge this paper describes the largest database of iris images for disease-affected eyes made publicly available to researchers and offers the most comprehensive study of what we can expect when iris recognition is employed for diseased eyes.

“…However, numerous medical conditions affecting the eye structures, especially the iris, anterior chamber of the eye, and the cornea, have a potential of degrading its accuracy and reliability. Yet, due to the lack of appropriate datasets and difficulties in creating them, limited research is available, mostly centered around cataract and cataract extraction procedure influence on iris recognition performance: (Roizenblatt et al, 2004), (Seyeddain et al, 2014), (Dhir et al, 2010), (Trokielewicz et al, 2014), (Ramachandra et al, 2016) (significant negative impact of cataract and cataract surgery reported by most researchers except for Dhir et al), impact of refraction correction procedures (Yuan et al, 2007) (no impact reported), but also studies regarding multiple disorders (Aslam et al, 2009), and their impact on segmentation (McConnon et al, 2012). In the papers (Trokielewicz et al, 2015b), we present the most thorough and comprehensive analysis on the subject of disease influence on iris recognition reliability to date, including an extensive cataract influence study, and a novel approach to eye pathology impact analysis, based not on disease taxonomy (impact of certain diseases), but rather on the type of damage that medical disorders afflict on the eye.…”

Section: Iris Biometrics and Ocular Disordersmentioning

confidence: 99%

Iris Recognition under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes

Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies

2017

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. a a Manuscript accepted for publication at the BIOSIGNALS 2017 conference