Coaxial optical structure for iris recognition from a distance

Supporting an unconstrained user interface is an important issue in iris recognition. Various methods try to remove the constraint of the iris being placed close to the camera, including portal-based and pan-tiltzoom (PTZ)-based solutions. Generally speaking, a PTZ-based system has two cameras: one scene camera and one iris camera. The scene camera detects the eye’s location and passes this information to the iris camera. The iris camera captures a high-resolution image of the person’s iris. Existing PTZ-based systems are divided into separate types and parallel types, according to how the scene camera and iris camera combine. This paper proposes a novel PTZ-based iris recognition system, in which the iris camera and the scene camera are combined in a coaxial optical structure. The two cameras are placed together orthogonally and a cold mirror is inserted between them, such that the optical axes of the two cameras become coincident. Due to the coaxial optical structure, the proposed system does not need the optical axis displacement-related compensation required in parallel-type systems. Experimental results show that the coaxial type can acquire an iris image more quickly and accurately than a parallel type when the stand-off distance is between 1.0 and 1.5 m. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University [R112002105070020 (2010)]

Image quality assessment for iris biometric

Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is the most reliable biometric in terms of recognition and identification performance. However, performance of these systems is affected by poor quality imaging. In this work, we extend previous research efforts on iris quality assessment by analyzing the effect of seven quality factors: defocus blur, motion blur, off-angle, occlusion, specular reflection, lighting, and pixel-counts on the performance of traditional iris recognition system. We have concluded that defocus blur, motion blur, and off-angle are the factors that affect recognition performance the most. We further designed a fully automated iris image quality evaluation block that operates in two steps. First each factor is estimated individually, then the second step involves fusing the estimated factors by using Dempster-Shafer theory approach to evidential reasoning. The designed block is tested on two datasets, CASIA 1.0 and a dataset collected at WVU. (Abstract shortened by UMI.)

Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking

The first step in monitoring an observer’s eye gaze is identifying and locating the image of their pupils in video recordings of their eyes. Current systems work under a range of conditions, but fail in bright sunlight and rapidly varying illumination. A computer vision system was developed to assist with the recognition of the pupil in every frame of a video, in spite of the presence of strong first-surface reflections off of the cornea. A modified Hough Circle detector was developed that incorporates knowledge that the pupil is darker than the surrounding iris of the eye, and is able to detect imperfect circles, partial circles, and ellipses. As part of processing the image is modified to compensate for the distortion of the pupil caused by the out-of-plane rotation of the eye. A sophisticated noise cleaning technique was developed to mitigate first surface reflections, enhance edge contrast, and reduce image flare. Semi-supervised human input and validation is used to train the algorithm. The final results are comparable to those achieved using a human analyst, but require only a tenth of the human interaction

Iris recognition method based on segmentation

The development of science and studies has led to the creation of many modern means and technologies that focused and directed their interests on enhancing security due to the increased need for high degrees of security and protection for individuals and societies. Hence identification using a person's vital characteristics is an important privacy topic for governments, businesses and individuals. A lot of biometric features such as fingerprint, facial measurements, acid, palm, gait, fingernails and iris have been studied and used among all the biometrics, in particular, the iris gets the attention because it has unique advantages as the iris pattern is unique and does not change over time, providing the required accuracy and stability in verification systems. This feature is impossible to modify without risk. When identifying with the iris of the eye, the discrimination system only needs to compare the data of the characteristics of the iris of the person to be tested to determine the individual's identity, so the iris is extracted only from the images taken. Determining correct iris segmentation methods is the most important stage in the verification system, including determining the limbic boundaries of the iris and pupil, whether there is an effect of eyelids and shadows, and not exaggerating centralization that reduces the effectiveness of the iris recognition system. There are many techniques for subtracting the iris from the captured image. This paper presents the architecture of biometric systems that use iris to distinguish people and a recent survey of iris segmentation methods used in recent research, discusses methods and algorithms used for this purpose, presents datasets and the accuracy of each method, and compares the performance of each method used in previous studie

Robust pre-processing techniques for non-ideal iris images

The human iris has been demonstrated to be a very accurate, non-invasive and easy-to-use biometric for personal identification. Most of the current state-of-the-art iris recognition systems require the iris acquisition to be ideal. A lot of constraints are hence put on the user and the acquisition process.;Our aim in this research is to relax these conditions and to develop a pre-processing algorithm, which can be used in conjunction with any matching algorithm to handle the so-called non-ideal iris images. In this thesis we present a few robust techniques to process the non-ideal iris images so as to give a segmented iris image to the matching algorithm. The motivation behind this work is to reduce the false reject rates of the current recognition systems and to reduce the intra-class variability. A new technique for estimating and compensating the angle in non-frontal iris images is presented. We have also developed a novel segmentation algorithm, which uses an ellipse-fitting approach for localizing the pupil. A fast and simple limbus boundary segmentation algorithm is also presented

Pupil Center Detection Approaches: A comparative analysis

In the last decade, the development of technologies and tools for eye tracking has been a constantly growing area. Detecting the center of the pupil, using image processing techniques, has been an essential step in this process. A large number of techniques have been proposed for pupil center detection using both traditional image processing and machine learning-based methods. Despite the large number of methods proposed, no comparative work on their performance was found, using the same images and performance metrics. In this work, we aim at comparing four of the most frequently cited traditional methods for pupil center detection in terms of accuracy, robustness, and computational cost. These methods are based on the circular Hough transform, ellipse fitting, Daugman's integro-differential operator and radial symmetry transform. The comparative analysis was performed with 800 infrared images from the CASIA-IrisV3 and CASIA-IrisV4 databases containing various types of disturbances. The best performance was obtained by the method based on the radial symmetry transform with an accuracy and average robustness higher than 94%. The shortest processing time, obtained with the ellipse fitting method, was 0.06 s.Comment: 15 pages, 9 figures, submitted to the journal "Computaci\'on y Sistemas