Biometric Recognition Using Deep Learning: A Survey <s> Iris Recognition <s> A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte "iris code". Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10/sup 31/. > <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Iris Recognition <s> This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endeavour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body its appearance is amenable to remote examination with the aid of a computer vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study where the system exhibits flawless performance in the evaluation of 520 iris images. > <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Iris Recognition <s> IriScan Inc. has for the past two years, been developing an identification/verification system capable of positively identifying and verifying the identity of individuals without physical contact or a person in the loop. Personal identification has historically been based on what a person possesses (a card); knows (a Personal Identification Number); or is (an inherent physiological or behavioral characteristic). Facial features (both infra-red signatures and geometry), fingerprints, hand geometry, vein patterns, retinal patterns, voice patterns, and signature dynamics have all been explored as biometric identifiers with varying levels of success. All but voice end facial recognition require contact or have been characterized by some as invasive or intrusive. Many have suffered from high cost and unsatisfactory error rates. A new technology, using the unique patterns of the human Iris, shows promise of overcoming previous shortcomings and providing positive identification of an individual without contact or invasion, at extremely high confidence levels. The video-based system locates the eye and Iris; evaluates the degree of occlusion by eyelid and spectral reflection; determines the quality of image focus; and determines the center and boundary of the pupil and the limbus (outer edge of the iris) for processing <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Iris Recognition <s> The personal identification approaches using iris images are receiving increasing attention in the biometrics literature. Several methods have been presented in the literature and those based on the phase encoding of texture information are suggested to be the most promising. However, there has not been any attempt to combine these approaches to achieve further improvement in the performance. This paper presents a comparative study of the performance from the iris authentication using Log-Gabor, Haar wavelet, DCT and FFT based features. Our experimental results suggest that the performance from the Haar wavelet and Log-Gabor filter based phase encoding is the most promising among all the four approaches considered in this work. Therefore, the combination of these two matchers is most promising, both in terms of performance and the computational complexity. Our experimental results from the all 411 users (CASIA v3) and 224 users (IITD v1) database illustrate significant improvement in the performance which is not possible with either of these approaches individually. <s> BIB004
Iris images contain a rich set of features embedded in their texture and patterns which do not change over time, such as rings, corona, ciliary processes, freckles, and the striated trabecular meshwork of chromatophore and fibroblast cells, which is the most prevailing under visible light BIB001 . Iris recognition has gained a lot of attention in recent years in different security-related fields. John Daugman developed one of the first modern iris recognition frameworks using 2D Gabor wavelet transform BIB004 . Iris recognition started to rise in popularity in the 1990s. In 1994, Wildes et al BIB002 introduced a device using iris recognition for personnel authentication. After that, many researchers started looking at iris recognition problem. Early works have used a variety of methods to extract hand-crafted features from the iris. Williams et al BIB003 converted all iris entries to an "IrisCode" and used Hamming's distance of an input iris image's IrisCode from those of the irises in the database as a metric for recognition. In  , the authors proposed an iris recognition system based on "deep scattering convolutional features", which achieved a significantly high accuracy rate on IIT Delhi dataset. This work is not exactly using deep learning, but is using a deep scattering convolutional network, to extract hierarchical features from the image. The output images at different nodes of scattering network denote the transformed image along different orientation and scales. The transformed images of the first and second layers of scattering transform for a sample iris image are shown in Figures 14 . These images are derived by applying bank of filters of 5 different scales and 6 orientations. It is worth mentioning that many of the classical iris recognition models perform several pre-processing steps such as iris detection, normalization, and enhancement, as shown in Figure 15 . They then extract features from the normalized or enhanced image. Many of the modern works on iris recognition skip normalization and enhancement, and yet, they are still able to achieve very high recognition accuracy. One reason is the ability of deep models to capture high-level semantic a features from original iris images, which are discriminative enough to perform well for iris recognition.