On Techniques for Angle Compensation in Nonideal Iris Recognition

Abstract

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

Implementation of Geodesic Active Contour Approach for Pigment Spots Segmentation on the Iris Surface

“…2.1. (2003) Geodesic active contour Roy et al (2007) Variational level sets Daugman (2003) Fourier-based approximation Schuckers et al (2007) Active shape models Pundlik et al (2008) Graph cuts Zuo and Schmid (2010) Ellipse fitting He et al (2013) Pulling and pushing model Du et al (2014) Scale invariants feature transform Masek et al (2015; Circular Hough transform Lim et al, 2014;Huang et al, 1990;Yuan et al (2005) Edge detection and Hough transform Huang et al (1990) Phase congruency and Hough transform Based on available documented literatures, Jillela and Ross (2013) had reported there is various iris segmentation approaches have been proposed to deal with the non-ideal eye image such as off-angled, occlusion and blurry motion situation as summarizes in Table 2.2. However, it is very rare to find documented publications on pigment spots.…”

Iris authentication in handheld devices - considerations for constraint-free acquisition

“…Realtime gaze compensation technique introduced by Daugman [25] or other techniques in literature can be used for this purpose [28], [29]. Schuckers et al [29] noted considerable improvement on iris recognition performance when gaze angle was compensated for images which are up to 15 o off-axis. Also, current gaze estimation techniques can estimate gaze angle with an average 3.5 o error over a 50 o range [30].…”

Multi-stage Visible Wavelength and Near Infrared Iris Segmentation Framework

“…While the image energy is defined in terms of the intensity difference on two sides of contour, shape energy tracks the difference between the current and average trained shape. Other approaches [6,15] have also identified the drawback of circular fitting, and use more complex shapes (like ellipses) or view-angle transformations to account for off-gaze.…”