Efficient Iris Recognition by Characterizing Key Local Variations

Abstract

Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

UBIRIS: A Noisy Iris Image Database

“…Wildes [26] proposes iris segmentation through a gradient based binary edge map construction followed by circular Hough transform. This methodology is the most widely used, being proposed with minor several variants in [3], [11], [14], [17], [19], [18] and [20]. [16] proposes one simple method based on thresholds and function maximization in order to obtain two ring parameters corresponding to iris inner and outer borders.…”

Two-Factor Authentication or How to Potentially Counterfeit Experimental Results in Biometric Systems

“…In order to apply biometric authentication we use our own implementation of the algorithm of Ma et al [16]. In their approach the iris texture is treated as a kind of transient signal which is processed using a 1-D wavelet transform.…”

“…We used N = 10 and M = 5 for our 512x64 pixel textures (only the 50 rows close to the pupil are used from the 64 rows, as suggested in [16] is then performed on each of the resulting 10 signals, and two fixed subbands are selected from each transform. This leads to a total of 20 subbands.…”

An Iterative Algorithm for Fast Iris Detection

“…Some use edge information [2] whereas others utilize thresholding [8]. Since the problem is trivial, most methods work well enough.…”

“…Despite the difficulties, transform-based techniques have been applied to iris detection in recognition [8,9]. Even though they can sometimes be quite efficiently implemented, a novel iterative approach is introduced to overcome the afroementioned difficulties.…”