No 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.

No abstract

No abstract

Iris recognition has been demonstrated to be an efficient technology for doing personal identification. Performance of iris recognition system depends on the isolation of the iris region from rest of the eye image. In this work, effective use of active shape models (ASMs) for doing iris segmentation is demonstrated. A method for building flexible model by learning patterns of iris invariability from a well organized training set is described. The specific approach taken in the work sacrifices generality, in order to accommodate better iris segmentation. The algorithm was initially applied on the on-angle, noise free CASIA data base and then was extended to the off-axis iris images collected at WVU eye center. A direct comparison with canny iris segmentation in terms of error rates, demonstrate effectiveness of ASM segmentation. For the selected threshold value of 0.4, FAR and FRR values were 0.13% and 0.09% using canny detectors and 0% each using the proposed ASM based method.