IrisSeg: A fast and robust iris segmentation framework for non-ideal iris images

Abstract

This paper presents a state-of-the-art iris segmentation framework specifically for non-ideal irises. The framework adopts coarse-to-fine strategy to localize different boundaries. In the approach, pupil is coarsely detected using an iterative search method exploiting dynamic thresholding and multiple local cues. The IntroductionIris is considered one of the most accurate biometrics because of its stability over long time, non-invasiveness, and unique pattern [1]. The accuracy of iris localization, decides the iris texture to be used for feature extraction and thus accuracy of overall recognition system, which makes this stage an important part of iris recognition. Daugman's Integro-Differential Operator (IDO) [1] and Wilde's circular Hough transform based approach [2] are the most significant and well known techniques for iris segmentation. Based on the basic steps in these two methods, many techniques have been proposed with some variations [5,7,9,14]. These classical and other similar approaches achieve excellent accuracy when iris images are of high-quality. However, in real world applications images may contain some types of challenges such as, occlusion, illumination variation, specular reflections, pupil constriction/expansion, image resolution, etc.. The prior art also discloses that almost all of such methods assume that pupillary and limbic boundaries are circular/ elliptical [1,2,14,18]. Recently, researchers discovered that in non-ideal iris images and even in ideal images, some shapes of pupil and iris are not perfect circle or ellipse [11]. Also, sometimes in non-ideal images, gradient on pupillary, iris and eyelid boundaries is not sufficient enough for these models. Thus, performance of such methods degrades significantly in non-ideal images. The other problems are that, these methods are time taking and computationally intensive due to brute-force approach and search of large parameter space. Moreover, parameters which are set to work for one database, may not work for other database. To address these issues, recently, researchers have started focusing towards non-ideal iris segmentation and various methods have been proposed [3][4][5][6][10][11][12][15][16][17] including adaptation of different types of active contours [11][12][13]19]. The active contours require the initial boundary from where it can proceed to localize actual boundary and a large number of iterations is a mandatory requirement, which makes the whole process a bit complex. For a detailed discussion on iris segmentation approaches and current issues, we refer the readers [7].In this paper, we propose an iris segmentation framework 1 which is simple to implement, robust, can work with non-ideal iris images without requirement of database specific parameter tuning. The major contributions include- Highly effective pupil detection module based on a Pupil Candidate Bank (PCB) created using iterative thresholding followed by selection of best candidate by exploiting multiple local features. Coarse approximation of limbi...

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

“…However, very few researchers have addressed these two databases for segmentation purposes due to unavailability of ground-truth images. To compare with other researchers, Gangwar et al [ 84 ] used various algorithms such as GST [ 85 ], Osiris [ 86 ], WAHET [ 87 ], IFFP [ 88 ], CAHT [ 89 ], Masek [ 90 ], and integro-differential operator (IDO) [ 25 ] with the same database. Therefore, for comparison, the comparative results with these algorithms are presented in Table 7 , and it can be found that the proposed IrisDenseNet outperforms other methods.…”

Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets

“…These are the off-axis subset and off-axis with unconstrained condition subset for Bath800 and CASIA Thousand and the off-axis subset for UBIRIS v2, as described in the section 2.3.3. In continuance it is compared with the segmentation results on these samples from the methods: SPDNN [43], IrisSeg [84] and OSIRIS [85]. The test set of the augmented samples is used to test the network and the other methods.…”

Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks

“…The IRISSEG [46] framework was designed specifically for non-ideal irises and is based on adaptive filtering, following a coarse-to-fine strategy. The authors emphasize that this approach does not require adjustment of parameters for different datasets.…”