Farmanullah Jan

Iris localization in frontal eye images for less constrained iris recognition systems

Commercial iris recognition systems do not perform well for non-ideal data, because their iris localization algorithms are specifically developed for controlled data. This paper presents a robust iris localization algorithm for less constrained data. It includes: (i) suppressing specular reflections; (ii) localizing the iris inner (pupil circle) and outer (iris circle) boundaries in a two-phase strategy. In the first phase, we use Hough transform, gray level statistics, adaptive thresholding, and a geometrical transform to extract the pupil circle in a sub-image containing a coarse pupil region. After that, we localize iris circle in a sub-image centered at the pupil circle. However, if the first phase fails, the second phase starts, where first we localize a coarse iris region in the eye image. Next, we extract pupil circle within the coarse iris region by reusing procedure of first phase. Following that, we localize iris circle. In either of the two phases, we validate the pupil location by using an effective occlusion transform; and (iii) regularizing the iris circular boundaries by using radial gradients and the active contours. Experimental results show that the proposed technique is tolerant to off-axis eye images, specular reflections, non-uniform illumination; glasses, contact lens, hair, eyelashes, and eyelids occlusions.

Reliable iris localization using Hough transform, histogram-bisection, and eccentricity

The iris technology recognizes individuals from their iris texture with great precision. However, it does not perform well for the non-ideal data, where the eye image may contain non-ideal issues such as the off-axis eye image, blurring, non-uniform illumination, hair, glasses, etc. It is because of their iris localization algorithms, which are developed for the ideal data. In this paper, we propose a reliable iris localization algorithm. It includes localizing a coarse iris location in the eye image using the Hough transform and image statistics; localizing the pupillary boundary using a bi-valued adaptive threshold and the two-dimensional (2D) shape properties; localizing the limbic boundary by reusing the Hough accumulator and image statistics; and finally, regularizing these boundaries using a technique based on the Fourier series and radial gradients. The proposed technique is tested on the public iris databases: CASIA V1, CASIA-IrisV3-Lamp, CASIA-IrisV4-Thousand, IITD V1.0, MMU V1.0, and MMU (new) V2.0. Experimental results obtained on these databases show superiority of the proposed technique over some state of the art iris localization techniques.

A dynamic non-circular iris localization technique for non-ideal data

Display Omitted Robust iris inner contour localization within a sliding window.A fast and reliable technique for the iris outer contour localization.Iris contours regularization.Tolerance to rotated-iris images, specular reflections, hair, eyelids, and glasses.Applicable in real-time applications. Iris localization plays a decisive role in the overall iris biometric systems performance, because it isolates the valid part of iris. This study proposes a reliable iris localization technique. It includes the following. First, it extracts the iris inner contour within a sliding-window in an eye image using a multi-valued adaptive threshold and the two-dimensional (2D) properties of binary objects. Then, it localizes the iris outer contour using an edge-detecting operator in a sub image centered at the pupil center. Finally, it regularizes the iris contours to compensate for their non-circular structure. The proposed technique is tested on the following public iris databases: CASA V1.0, CASIA-Iris-Lamp, IITD V1.0, and the MMU V1.0. The experimental and accuracy results of the proposed scheme compared with other state-of-the-art techniques endorse its satisfactory performance.

Segmentation and localization schemes for non-ideal iris biometric systems

Contemporary surveys focus on the ideal iris biometric systems, which are generally used for overt applications: e.g., border crossing control. Notably, these systems perform well for ideal data, but poorly for non-ideal data. It is because that their iris segmentation modules are generally developed for ideal data, which is acquired under controlled environment using the near infra-red (NIR) illumination only. On other hand, a subject could relatively be on-the-move and/or at-a-distance from the image acquisition device in non-ideal systems. Non-ideal systems use either the visible wavelength (VW) or NIR illumination while acquiring image data. Non-ideal images may contain noisy factors such as off-axis and off-angle eyeimages, non-circular iris boundaries, and non-uniform illumination. For secure and reliable security measures, non-ideal iris biometric systems are strongly desired around the globe in particular for the covert applications, e.g., monitoring the terrorists activities. It is observed that literature encompasses research work from the prominent journals in this concern until 2010; therefore, this study includes significant publications following 2010 for both NIR and VW image data. Notably, the main objective of this survey is to offer a quick-reference regarding recent developments in non-ideal iris biometrics, supplemented with some indispensible prerequisites for beginners and professional researchers. A survey of non-ideal iris segmentation/localization schemes since 2010.Schemes for the visible wavelength iris databases.Schemes for the near infra-red iris databases as well.Helpful for both the beginners and seasonal researchers.A reference document to the latest research work in iris biometric.

Non-circular iris contours localization in the visible wavelength eye images

Effective non-circular iris contours localization algorithm.Validated on the visible wavelength iris databases.Validated on the near infra-red iris databases.It is real-time applicable. Contemporary iris localization schemes perform well for ideal eye images generally acquired using near infrared (NIR) illumination, but perform poorly for non-ideal eye images acquired using Visible Wavelength (VW) illumination. Non-ideal eye images contain noise such as non-uniform illumination, defocus, etc. This study proposes a reliable iris localization algorithm, in particular for VW eye images. First, it suppresses light reflections and sharp intensity variations in the Red-component of input color eye image. Next, it marks a set of potential pixels (ROIpixels) suitable as the iris centers candidates. Using ROIpixels, it localizes potential iris location using a hybrid of Integro-differential operator (IDO) and image intensity. Then, it reuses IDO and iris/pupils geometry to localize pupil within iris. Finally, it uses the Fourier series based scheme to regularize iris contours. Experimental results obtained on UBIRIS V1.0, MMU V1.0, and IITD V1.0 iris databases demonstrate its superior performance compared with contemporary schemes. Display Omitted

Pupil localization in image data acquired with near-infrared or visible wavelength illumination

Pupil localization in human face/eye images has numerous applications, e.g., eye tracking, iris recognition, cataract assessment and surgery, diabetic retinopathy screening, neuropsychiatric disorders diagnosing, and aliveness detection. In real scenario, the pupil localization task suffers from many complications such as pupil’s constriction and dilation moments, light reflections, eyelids and eyelashes, and cataract disease. To resolve this issue, this study proposes an accurate and fast pupil localization scheme. It performs relatively well for eyeimages acquired either with the near infrared (NIR) or visible wavelength (VW) illumination. First, it effectively preprocesses the input eyeimage. Next, it coarsely marks pupil location using a scheme comprising an adaptive threshold and two-dimensional (2D) object properties. Then, it validates pupil location via an effective test involving global gray-level statistics. If it finds pupil location invalid, then it localizes pupil through a hybrid of the Hough transform and image global gray-level statistics. Finally, it localizes the fine pupillary boundary through a hybrid of the Fourier series and image’s gradients. Its experimental results obtained on numerous publically available iris datasets demonstrate its superiority over most of the contemporary schemes.