Impact of (segmentation) quality on long vs. short‐timespan assessments in iris recognition performance

Abstract

Several researchers have presented studies of temporal effects on iris recognition accuracy, with varying results on severity of observed effects. The sensitive topic continues to be adversely discussed and the difficulty of isolating performance-impacting factors is immanent. The impact of ageing on segmentation versus feature extraction has been largely neglected so far. This paper attempts to shed light on the impact of segmentation quality on observed temporal effects highlighting the critical role of the segmentation module and quality assessment when assessing ageing effects. The lack of large and standardized temporal variation in public datasets as well as additional metadata (age of subject, recording parameters) and strictly enforced unified recording and quality guidelines over time-separated sessions are identified as imminent problems of ageing studies. Results are reported on a long-timespan database of 36,240 images comprising 104 classes and a 4-year time lapse, offering a large variety of recording conditions highlighting the critical role of a transparent recording setup.

Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset

“…By the design, this dataset is biased toward better performing users, as it contains mainly the data from travellers who did not experience problems with the system and does not contain any rejected transactions. Nevertheless, even with this limitation, this dataset presents a unique and very valuable source for investigation of iris biometrics properties and limitations, specifically related to age and ageing, which becomes particularly important now with iris modality becoming increasingly used in many government and United Nations programmes [11, 12] and the ongoing debate related to the tolerance of iris biometrics to ageing [3–9, 13–21].…”

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

“…NIST's IREX VI report, however, states the contrary (Grother et al, 2013), and was later criticized by Bowyer (Bowyer and Ortiz, 2013), and a response to that critique was also published (Grother et al, 2015). Recently, more researchers have made efforts to better understand the non-stationarity of templates, namely by isolating as many factors as possible (Hofbauer et al, 2016), studying the impact of segmentation quality (Wild et al, 2015), but also the influence of sensor aging (Bergmuller et al, 2014). This shows that despite many research efforts having been put into solving these issues, template aging still presents many challenges, and new solutions and experimental methodologies are much welcome.…”

Isolating Iris Template Ageing in a Semi-Controlled Environment

“…The full original database contains 120 images per eye and user from video sequences captured in 2009, and 20 images per eye and user from video sequences captured in 2013. Wild et al [10] used the full database to analyse the impact of segmentation and quality on iris recognition performance. Since our goal is to eliminate as many acquisition effects as possible, we used the results from their work to guide our selection.…”