A Design Paradigm for Long Range Iris Recognition Systems with Sparsity Based Techniques for Iridal Texture Enhancement

2018-11-14T20:14:17Z (GMT) by Shreyas Venugopalan
The role of biometrics, as an integral component of security infrastructures, has been firmly established in today's world. Its ability to accurately identify people from a distance has served as an important life-saving tool in the arsenal<br>of our men and women in uniform, out in war theaters; it has also served as an effective forensics tool for our police force and off-late, is being increasingly adopted to secure sensitive transactions such as those with financial institutions.<br>Traditionally, fingerprint patterns and facial photographs have held sway over identity veri fication tools; the former, because of its uniqueness and the latter because of its ubiquity. However, over the past decade, iris patterns have also<br>come to the fore because of the combined advantage of uniqueness across the populace as well as the ability to image these patterns from a distance. Today most commercial iris acquisition systems report recognition accuracies upwards of 90%, over entire populations. However, the caveat is that these systems are<br>constrained to operate very close to the user, owing to the challenges involved in achieving sufficient resolution over the iride texture from large distances. This dissertation aims to push the boundaries of iris imaging devices and in-<br>troduces theoretical and practical considerations in designing a novel long range iris recognition system. The aim is to design a system capable of acquiring enrollment quality iris images, similar in quality to those captured by short range systems such as IriShield, LG, PIER, HIIDE systems, at much larger stand-off distances. The challenges involved in acquiring images with sufficient spatial<br>resolution, when subjects are too far out from the optimal acquisition range, are explored and potential solutions are described. First, we describe the design methodology behind creating a long range iris system, to aid the reader in<br>understanding the key design considerations when using available commercial o -the-shelf hardware. Competing objectives such as achieving high magnifi cation while maintaining sufficient depth of field, using a sensor with large pixel pitch for low noise performance while resolving the required patterns, etc. are discussed. As a result of this work, the rst long range iris acquisition system of its kind, capable of acquiring enrollment iris images from up to 9m<br>and matching up to 12m, has been designed and built. We show the system, present lessons learned during the design process as well as empirical results for the imaging performance. The latter half of the dissertation develops an<br>iris texture enhancement algorithm for restoring degraded iris images - specifically noisy, low-resolution and out-of-focus/blurred images, which are often encountered in the real world. The method is developed and evaluated using<br>synthesized degraded images from the NIST Iris Challenge Evaluation (ICE) database. A signifi cant improvement in verification performance is also seen, when using this enhancement method, on actual degraded images captured with the proposed long range iris system. The proposed enhancement method is modeled as a linear regression problem that exploits the sparsity of iris textures in<br>an over-complete dictionary. Improvements in iris recognition performance using the enhanced textures are studied in the latter half of this dissertation. In summary, this work enhances and exceeds the capabilities of current iris imaging systems for the explicit purpose of accurately identifying threats using iride patterns, from distances larger than are currently possible. This will serve as a<br>necessary tool in the arsenal of security establishments to identify threats, save lives and to protect our freedoms for generations to come.<div><br> </div>