Hector J. Santos-Villalobos

Effects of iris surface curvature on iris recognition

To focus on objects at various distances, the lens of the eye must change shape to adjust its refractive power. This change in lens shape causes a change in the shape of the iris surface which can be measured by examining the curvature of the iris. This work isolates the variable of iris curvature in the recognition process and shows that differences in iris curvature degrade matching ability. To our knowledge, no other work has examined the effects of varying iris curvature on matching ability. To examine this degradation, we conduct a matching experiment across pairs of images with varying degrees of iris curvature differences. The results show a statistically significant degradation in matching ability. Finally, the real world impact of these findings is discussed.

Statistical characterization and segmentation of drusen in fundus images

Age related Macular Degeneration (AMD) is a disease of the retina associated with aging. AMD progression in patients is characterized by drusen, pigmentation changes, and geographic atrophy, which can be seen using fundus imagery. The level of AMD is characterized by standard scaling methods, which can be somewhat subjective in practice. In this work we propose a statistical image processing approach to segment drusen with the ultimate goal of characterizing the AMD progression in a data set of longitudinal images. The method characterizes retinal structures with a statistical model of the colors in the retina image. When comparing the segmentation results of the method between longitudinal images with known AMD progression and those without, the method detects progression in our longitudinal data set with an area under the receiver operating characteristics curve of 0.99.

Limbus impact on off-angle iris degradation

The accuracy of iris recognition depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. Off-angle iris recognition is a new research focus in biometrics that tries to address several issues including corneal refraction, complex 3D iris texture, and blur. In this paper, we present an additional significant challenge that degrades the performance of the off-angle iris recognition systems, called the “limbus effect”. The limbus is the region at the border of the cornea where the cornea joins the sclera. The limbus is a semitransparent tissue that occludes a side portion of the iris plane. The amount of occluded iris texture on the side nearest the camera increases as the image acquisition angle increases. Without considering the role of the limbus effect, it is difficult to design an accurate off-angle iris recognition system. To the best of our knowledge, this is the first work that investigates the limbus effect in detail from a biometrics perspective. Based on results from real images and simulated experiments with real iris texture, the limbus effect increases the hamming distance score between frontal and off-angle iris images ranging from 0.05 to 0.2 depending upon the limbus height.

Gaze estimation for off-angle iris recognition based on the biometric eye model

Iris recognition is among the highest accuracy biometrics. However, its accuracy relies on controlled high quality capture data and is negatively affected by several factors such as angle, occlusion, and dilation. Non-ideal iris recognition is a new research focus in biometrics. In this paper, we present a gaze estimation method designed for use in an off-angle iris recognition framework based on the ORNL biometric eye model. Gaze estimation is an important prerequisite step to correct an off-angle iris images. To achieve the accurate frontal reconstruction of an off-angle iris image, we first need to estimate the eye gaze direction from elliptical features of an iris image. Typically additional information such as well-controlled light sources, head mounted equipment, and multiple cameras are not available. Our approach utilizes only the iris and pupil boundary segmentation allowing it to be applicable to all iris capture hardware. We compare the boundaries with a look-up-table generated by using our biologically inspired biometric eye model and find the closest feature point in the look-up-table to estimate the gaze. Based on the results from real images, the proposed method shows effectiveness in gaze estimation accuracy for our biometric eye model with an average error of approximately 3.5 degrees over a 50 degree range.

Off-angle iris correction using a biological model

This work implements an eye model to simulate corneal refraction effects. Using this model, ray tracing is performed to calculate transforms to remove refractive effects in off-angle iris images when reprojected to a frontal view. The correction process is used as a preprocessing step for off-angle iris images for input to a commercial matcher. With this method, a match score distribution mean improvement of 11.65% for 30 degree images, 44.94% for 40 degree images, and 146.1% improvement for 50 degree images is observed versus match score distributions with unmodified images.

Non-Uniform Contrast and Noise Correction for Coded Source Neutron Imaging

Since the first application of neutron radiography in the 1930s, the field of neutron radiography has matured enough to develop several applications. However, advances in the technology are far from concluded. In general, the resolution of scintillator-based detection systems is limited to the 10μm range, and the relatively low neutron count rate of neutron sources compared to other illumination sources restricts time resolved measurement. One path toward improved resolution is the use of magnification; however, to date neutron optics are inefficient, expensive, and difficult to develop. There is a clear demand for cost-effective scintillator-based neutron imaging systems that achieve resolutions of 1μm or less. Such imaging system would dramatically extend the application of neutron imaging. For such purposes a coded source imaging system is under development. The current challenge is to reduce artifacts in the reconstructed coded source images. Artifacts are generated by non-uniform illumination of the source, gamma rays, dark current at the imaging sensor, and system noise from the reconstruction kernel. In this paper, we describe how to pre-process the coded signal to reduce noise and non-uniform illumination, and how to reconstruct the coded signal with three reconstruction methods correlation, maximum likelihood estimation, and algebraic reconstruction technique. We illustrates our results with experimental examples.

Coded source neutron imaging

Coded aperture techniques have been applied to neutron radiography to address limitations in neutron flux and resolution of neutron detectors in a system labeled coded source imaging (CSI). By coding the neutron source, a magnified imaging system is designed with small spot size aperture holes (10 and 100μm) for improved resolution beyond the detector limits and with many holes in the aperture (50% open) to account for flux losses due to the small pinhole size. An introduction to neutron radiography and coded aperture imaging is presented. A system design is developed for a CSI system with a development of equations for limitations on the system based on the coded image requirements and the neutron source characteristics of size and divergence. Simulation has been applied to the design using McStas to provide qualitative measures of performance with simulations of pinhole array objects followed by a quantitative measure through simulation of a tilted edge and calculation of the modulation transfer function (MTF) from the line spread function. MTF results for both 100μm and 10μm aperture hole diameters show resolutions matching the hole diameters.

Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well’s health and prediction of high-pressure hydraulic f...

An Iris Segmentation Algorithm based on Edge Orientation for Off-angle Iris Recognition

Iris recognition is known as one of the most accurate and reliable biometrics. However, the accuracy of iris recognition systems depends on the quality of data capture and is negatively affected by several factors such as angle, occlusion, and dilation. In this paper, we present a segmentation algorithm for off-angle iris images that uses edge detection, edge elimination, edge classification, and ellipse fitting techniques. In our approach, we first detect all candidate edges in the iris image by using the canny edge detector; this collection contains edges from the iris and pupil boundaries as well as eyelash, eyelids, iris texture etc. Edge orientation is used to eliminate the edges that cannot be part of the iris or pupil. Then, we classify the remaining edge points into two sets as pupil edges and iris edges. Finally, we randomly generate subsets of iris and pupil edge points, fit ellipses for each subset, select ellipses with similar parameters, and average to form the resultant ellipses. Based on the results from real experiments, the proposed method shows effectiveness in segmentation for off-angle iris images.

Progress Implementing a Model-Based Iterative Reconstruction Algorithm for Ultrasound Imaging of Thick Concrete

All commercial nuclear power plants (NPPs) in the United States contain concrete structures. These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and the degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Concrete structures in NPPs are often inaccessible and contain large volumes of massively thick concrete. While acoustic imaging using the synthetic aperture focusing technique (SAFT) works adequately well for thin specimens of concrete such as concrete transportation structures, enhancements are needed for heavily reinforced, thick concrete. We argue that image reconstruction quality for acoustic imaging in thick concrete could be improved with Model-Based Iterative Reconstruction (MBIR) techniques. MBIR works by designing a probabilistic model for the measurements (forward model) and a probabilistic model for the object (prior model). Both models are used to formulate an objective...