Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing

Abstract

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

Iris Recognition Using Fuzzy Level Set and GEFE

“…The iris contour may differ depending on the image acquisition techniques [2]- [5]. We divide the iris segmentation process into three steps.…”

“…In [6], inner and outer boundaries were isolated using the discrete Fourier series expansions of the contour data. The modified Mumford-Shah segmentation model was deployed in [2] to localize the iris and pupil boundary. A traditional active contour model and the Hough transform technique were applied to segment the iris region in [7].…”

“…The parametric active contour-based curve evolution process may be interrupted at a certain local minima such as the specular reflections, the thick radial fibres in the iris or the crypts in the ciliary region [8]. The active contours with an edge stopping function as halting criteria may fail to isolate the weak outer boundary [2], [8].…”

Iris Recognition Using Level Set and Local Binary Pattern

“…The segmentation of the nonideal iris image is a difficult task because of the noncircular shape of the pupil and the iris, and the shape differs depending on the image acquisition techniques [2]- [5]. We divide the iris segmentation process into three steps.…”

“…Furthermore, the iris contours of the noisy irises are not exactly circular or elliptical and may be of any kind of shapes [2]- [6]. To mitigate the shape irregularities of the iris contours, several researchers proposed different iris segmentation methods based on active contours, including the modified Mumford-Shah segmentation model [2], Variational Level Set (VLS) [3], Fourier series expansions of the contour data [4], and Geodesic Active Contours (GAC) [5].…”