Estefan Ortiz

Template Aging Phenomenon in Iris Recognition

Biometric template aging is defined as an increase in recognition error rate with increased time since enrollment. It is believed that template aging does not occur for iris recognition. Several research groups, however, have recently reported experimental results showing that iris template aging does occur. This template aging effect manifests as a shift in the authentic distribution, resulting in an increased false non-match rate. Analyzing results from a three-year time-lapse data set, we find ~150% increase in the false non-match rate at a decision threshold representing a one in two million false match rate. We summarize several known elements of eye aging that could contribute to template aging, including age-related change in pupil dilation. Finally, we discuss various steps that can control the template aging effect in typical identity verification applications.

A linear regression analysis of the effects of age related pupil dilation change in iris biometrics

Medical studies have shown that average pupil size decreases linearly throughout adult life. Therefore, on average, the longer the time between acquisition of two images of the same iris, the larger the difference in dilation between the two images. Several studies have shown that increased difference in dilation causes an increase in the false nonmatch rate for iris recognition. Thus, increased difference in pupil dilation is one natural mechanism contributing to an iris template aging effect. We present an experimental analysis of the change in genuine match scores in the context of dilation differences due to aging.

Dilation aware multi-image enrollment for iris biometrics

Current iris biometric systems enroll a person based on the best eye image taken at the time of acquisition. However, recent research has shown that simply taking the best eye image and ignoring pupil dilation leads to degradations in system performance. In particular, the probability of a false non-match increases when there is a considerable variation in pupil size between the enrolled eye image and the probe eye image. Therefore, methods of enrollment that take into account pupil dilation are needed to ensure reliability of an iris biometric system. Our research examines a strategy to improve system performance by implementing a dilation-aware enrollment phase that chooses eye images based on their respective empirical dilation ratio distribution. We compare our strategy of enrollment to that of the randomly chosen eye images, which is the current enrollment procedure for most iris biometric systems. Our results show that there is a noticeable improvement over the random scenario when pupil dilation is accounted for during the enrollment phase.

Exploratory analysis of an operational iris recognition dataset from a CBSA border-crossing application

This paper presents an exploratory analysis of an iris recognition dataset from the NEXUS border-crossing program run by the Canadian Border Services Agency. The distribution of the normalized Hamming distance for successful border-crossing transactions is examined in the context of various properties of the operational scenario. The effects of properties such as match score censoring and truncation, same-sensor and cross-sensor matching, sequence-dependent matching, and multiple-kiosk matching are illustrated. Implications of these properties of the operational dataset for the study of iris template aging are discussed.

Critical examination of the IREX VI results

The authors analyse why Iris Exchange Report (IREX) VI conclusions about ‘iris ageing’ differ significantly from results of previous research on ‘iris template ageing’. They observe that IREX VI uses a definition of ‘iris ageing’ that is restricted to a subset of International Organization for Standardization (ISO)-definition template ageing. They also explain how IREX VI commits various methodological errors in obtaining what it calls its ‘best estimate of iris recognition ageing’. The OPS-XING dataset that IREX VI analyses for its ‘best estimate of iris recognition ageing’ contains no matches with Hamming distance >0.27. A ‘truncated regression’ technique should be used to analyse such a dataset, which IREX VI fails to do so, biasing its ‘best estimate’ to be lower-than-correct. IREX VI mixes Hamming distances from first, second and third attempts together in its regression, creating another source of bias towards a lower-than-correct value. In addition, the match scores in the OPS-XING dataset are generated from a ‘1-to-first’ matching strategy, meaning that they contain a small but unknown number of impostor matches, constituting another source of bias towards an artificially low value for ageing. Finally, IREX VI makes its ‘best estimate of iris recognition ageing’ by interpreting its regression model without taking into account the correlation among independent variables. This is another source of bias towards an artificially low value for ageing. Importantly, the IREX VI report does not acknowledge the existence of any of these sources of bias. They conclude with suggestions for a revised, improved IREX VI.

Trial Somaliland voting register de-duplication using iris recognition

Face and fingerprint were used in de-duplication of the voter registration list for the 2010 Somaliland presidential election. Iris recognition was evaluated as a possible more powerful means of de-duplication of the voting register for the planned 2015 elections. On a trial dataset of 1,062 registration records, all instances of duplicate registration were detected and zero non-duplicates were falsely classified as duplicates, indicating the power of iris recognition for voting register de-duplication. All but a tiny fraction of the cases were classified by automatic matching, and the remaining cases were classified by forensic iris matching. Images in this dataset reveal the existence of unusual eye conditions that consistently cause false-non-match results. Examples are shown and discussed.

An optimal strategy for dilation based iris image enrollment

The progression of research into understanding and mitigating the effects of pupil dilation on iris biometrics is at a point where a formalization of the problem is necessary to tie together several research directions and results. Past research has shown that differences in dilation in a (probe, gallery) pair lead to an increase in false non-match rates. Additionally, analysis continues to show that there is at least an approximate linear relationship between increase in dilation difference and degradation in match scores. Lastly, dilation-aware based enrollment techniques have shown to be a promising approach to addressing matching errors due to pupil dilation difference. This paper establishes a framework based on an assumed linear relationship between match scores and dilation difference and shows that the optimal image to enroll based on pupil dilation is the image which has a dilation value near the mean or median depending on the measure of dilation difference.

Dilation-aware enrolment for iris recognition

Iris recognition systems typically enrol a person based on a single ‘best’ eye image. Research has shown that the probability of a false non-match result increases with increased difference in pupil dilation between the enrolment image and the probe image. Therefore, dilation-aware methods of enrolment should improve the accuracy of iris recognition. The authors examine a strategy to improve accuracy through a dilation-aware enrolment step that selects one or more enrolment images based on the observed distribution of dilation ratios for that eye. Additionally, they demonstrate that an image with median dilation is the optimal single eye image dilation-aware enrolment choice. Their results confirm that this dilation-aware enrolment strategy does improve matching accuracy compared with traditional single-image enrolment, and also compared with multi-image enrolment that does not take dilation into account.

Pitfalls in studying “big data” from operational scenarios

Analyzing a larger dataset is sometimes assumed, in itself, to confer a greater degree of validity to the results of a study. In biometrics, analyzing an “operational” dataset is also sometimes assumed, in itself to confer a greater degree of validity. And so studying a large, operational biometric dataset may seem to guarantee valid results. However, a number of basic questions should be asked of any “found” big data, in order to avoid pitfalls of the data not being suitable for the desired analysis. We explore such issues using a large operational iris recognition dataset from the Canada Border Services Agencys NEXUS program, similar to the dataset analyzed in the NISTIREX VI report.

Iris recognition technology evaluated for voter registration in Somaliland

The Somaliland government web site includes the slogan: ‘Recognition – The number one priority for the Somaliland government’. Part of the effort to achieve international recognition includes holding elections that are respected as fair and that enable peaceful, political transitions. This article summarises the role that biometrics – face, finger, and iris – is playing in creating voter registration lists in Somaliland. Specifically, this article describes Somalilands efforts to move to iris recognition in an effort to create a voter registration list for the upcoming elections scheduled in 2015.