Robust Long Range Iris Recognition from Video Using Super Resolution

Iris recognition has been developing for over 20 years, but, only in recent years has it been more accessible and widely accepted as one of the most accurate and un-obtrusive biometric modalities. Over the past few years, many companies have developed iris acquisition systems that are more user-friendly. Iris-On-the-Move (IOM) is one such system, offering significant stand-off acquisition distance (3m), which is extremely convenient for users and very suitable for deployment at airports to check passenger identifications and to control access. However, iris images acquired by the IOM and other long range systems are, in most cases, considerably blurred, of low contrast, and lacking detail in the iris texture compared to images from very close proximity sensors (5cm stand-off). This thesis focuses on how to deal with the three most challenging problems in long-range iris recognition: (1) iris segmentation from long range systems, (2) automatic iris mask generation of occluded regions, (3) iris matching performance enhancement using multiple irises from a video sequence. In particular, we emphasize solutions in the context of the IOM system and those that take advantage of an iris image video stream. If an image of the eye is clear and has strong contrast, it is very easy to find the boundaries of the pupil and iris. However, most images acquired by the longrange iris acquisition system are blurred and noisy, which is the first problem that we propose a solution to: iris segmentation. Even worse, there are always strong specular reflections either in the pupil or on the iris region, which increases the difficulty in achieving good iris segmentation results. For this problem, we propose a novel iris segmentation algorithm which is robust in dealing with specular reflections and image blur and is also computationally efficient. For the second problem, automatic detection of iris occluded regions from eyelashes and specular reflections, we focus on estimating a mask for the iris texture in the polar coordinate system. Unlike most current methods, we propose a probabilistic, learning-based approach where the system can learn about the pixel distribution from a training data set and create masks for a test data set in an efficient way. We further search the parameter space of Gabor filters in order to optimize the features set that the prposed algorithm learns, for the purpose of minimizing global error rate for large-scale iris recognition. Iris matching performance enhancement for images captured by long-range iris acquisition devices, the third problem we address, deals with iris images that are blurred, defocused, and noisy due to low quantum efficiency of long-range iris sensors imaging in near-Infrared wavelengths. By exploiting the multi-frame video capture of the long-range iris acquisition system, it is possible to enhance the recognition performance by improving the low-quality iris images acquired from the system using super-resolution methods designed specifically for irises. We show a comprehensive set of empirical results demonstrating the effectiveness of our proposed approach designed for the IOM system that also apply to any video sequence of iris images captured by long-range iris acquisition devices.