Enhancing Iris Matching Using Levenshtein Distance with Alignment Constraints

Abstract

Abstract. Iris recognition from surveillance-type imagery is an active research topic in biometrics. However, iris identification in unconstrained conditions raises many proplems related to localization and alignment, and typically leads to degraded recognition rates. While development has mainly focused on more robust preprocessing, this work highlights the possibility to account for distortions at matching stage. We propose a constrained version of the Levenshtein Distance (LD) for matching of binary iris-codes as an alternative to the widely accepted Hamming Distance (HD) to account for iris texture distortions by e.g. segmentation errors or pupil dilation. Constrained LD will be shown to outperform HD-based matching on CASIA (third version) and ICE (2005 edition) datasets. By introducing LD alignment constraints, the matching problem can be solved in O(n · s) time and O(n + s) space with n and s being the number of bits and shifts, respectively.

A Robust Iris Feature Extraction Approach Based on Monogenic and 2D Log-Gabor Filters

“…Tables 1 and 2 show that the proposed method using the dissimilarity modified HD improves the accuracy of iris recognition, with FAR = 3% and FRR = 3% compared with Uhl and Wild's [34], Khanfir's fractal analysis [18,19], and Khanfir's Meyer wavelet [18]. Moreover, our method with the modified HD is more accurate than the cosine similarity measure, as shown in Tables 3 and 4.…”

“…Several methods of the state of art [1,4,21,34] never ceased to improve the method of Masek as one of the famous circle model techniques. Some of them tried to optimize its parameters [4,29].…”

“…Some of them tried to optimize its parameters [4,29]. Uhl and Wild enhanced the iris matching process [34]. Liu et al improved the segmentation process by reversing the detection order between outer and inner iris boundaries and by bringing some changes to the Hough transform [21].…”

Iris-biometric comparators: Exploiting comparison scores towards an optimal alignment under Gaussian assumption

“…Obviously, applying more than one enrollment sample yields better recognition performance [4]. Uhl and Wild [9] have proposed the use of a constrained version of the Levenshtein distance to tolerate e.g. segmentation inaccuracies or non-linear deformations by employing inexact matching.…”

“…Obviously, the proposed technique requires additional computational effort over the MinHD comparator, however, extra cost is kept low compared to other proposed approaches (e.g. [10,9]). Fig.…”