Multispectral scleral patterns for ocular biometric recognition

Biometrics is the science of recognizing people based on their physical or behavioral traits such as face, fingerprints, iris, and voice. Among the various traits studied in the literature, ocular biometrics has gained popularity due to the significant progress made in iris recognition. However, iris recognition is unfavorably influenced by the non-frontal gaze direction of the eye with respect to the acquisition device. In such scenarios, additional parts of the eye, such as the sclera (the white of the eye) may be of significance. In this dissertation, we investigate the use of the sclera texture and the vasculature patterns evident in the sclera as potential biometric cues. Iris patterns are better discerned in the near infrared spectrum (NIR) while vasculature patterns are better discerned in the visible spectrum (RGB). Therefore, multispectral images of the eye, consisting of both NIR and RGB channels, were used in this work in order to ensure that both the iris and the vasculature patterns are successfully imaged.;The contributions of this work include the following. Firstly, a multispectral ocular database was assembled by collecting high-resolution color infrared images of the left and right eyes of 103 subjects using the DuncanTech MS 3100 multispectral camera. Secondly, a novel segmentation algorithm was designed to localize the spacial extent of the iris, sclera and pupil in the ocular images. The proposed segmentation algorithm is a combination of region-based and edge-based schemes that exploits the multispectral information. Thirdly, different feature extraction and matching method were used to determine the potential of utilizing the sclera and the accompanying vasculature pattern as biometric cues. The three specific matching methods considered in this work were keypoint-based matching, direct correlation matching, and minutiae matching based on blood vessel bifurcations. Fourthly, the potential of designing a bimodal ocular system that combines the sclera biometric with the iris biometric was explored.;Experiments convey the efficacy of the proposed segmentation algorithm in localizing the sclera and the iris. The use of keypoint-based matching was observed to result in the best recognition performance for the scleral patterns. Finally, the possibility of utilizing the scleral patterns in conjunction with the iris for recognizing ocular images exhibiting non-frontal gaze directions was established

Characterization of extrasolar terrestrial planets from diurnal photometric variability

The detection of massive planets orbiting nearby stars has become almost routine, but current techniques are as yet unable to detect terrestrial planets with masses comparable to the Earth's. Future space-based observatories to detect Earth-like planets are being planned. Terrestrial planets orbiting in the habitable zones of stars-where planetary surface conditions are compatible with the presence of liquid water-are of enormous interest because they might have global environments similar to Earth's and even harbor life. The light scattered by such a planet will vary in intensity and colour as the planet rotates; the resulting light curve will contain information about the planet's properties. Here we report a model that predicts features that should be discernible in light curves obtained by low-precision photometry. For extrasolar planets similar to Earth we expect daily flux variations up to hundreds of percent, depending sensitively on ice and cloud cover. Qualitative changes in surface or climate generate significant changes in the predicted light curves. This work suggests that the meteorological variability and the rotation period of an Earth-like planet could be derived from photometric observations. Other properties such as the composition of the surface (e.g., ocean versus land fraction), climate indicators (for example ice and cloud cover), and perhaps even signatures of Earth-like plant life could be constrained or possibly, with further study, even uniquely determined.Comment: Published in Nature. 9 pages including 3 figure

Monte Carlo simulations of soft proton flares: testing the physics with XMM-Newton

Low energy protons (<100-300 keV) in the Van Allen belt and the outer regions can enter the field of view of X-ray focusing telescopes, interact with the Wolter-I optics, and reach the focal plane. The use of special filters protects the XMM-Newton focal plane below an altitude of 70000 km, but above this limit the effect of soft protons is still present in the form of sudden flares in the count rate of the EPIC instruments, causing the loss of large amounts of observing time. We try to characterize the input proton population and the physics interaction by simulating, using the BoGEMMS framework, the proton interaction with a simplified model of the X-ray mirror module and the focal plane, and comparing the result with a real observation. The analysis of ten orbits of observations of the EPIC/pn instrument show that the detection of flares in regions far outside the radiation belt is largely influenced by the different orientation of the Earth's magnetosphere respect with XMM-Newton's orbit, confirming the solar origin of the soft proton population. The Equator-S proton spectrum at 70000 km altitude is used for the proton population entering the optics, where a combined multiple and Firsov scattering is used as physics interaction. If the thick filter is used, the soft protons in the 30-70 keV energy range are the main contributors to the simulated spectrum below 10 keV. We are able to reproduce the proton vignetting observed in real data-sets, with a 50\% decrease from the inner to the outer region, but a maximum flux of 0.01 counts cm-2 s-1 keV-1 is obtained below 10 keV, about 5 times lower than the EPIC/MOS detection and 100 times lower than the EPIC/pn one. Given the high variability of the flare intensity, we conclude that an average spectrum, based on the analysis of a full season of soft proton events is required to compare Monte Carlo simulations with real events

Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network

Robust pre-processing techniques for non-ideal iris images

The human iris has been demonstrated to be a very accurate, non-invasive and easy-to-use biometric for personal identification. Most of the current state-of-the-art iris recognition systems require the iris acquisition to be ideal. A lot of constraints are hence put on the user and the acquisition process.;Our aim in this research is to relax these conditions and to develop a pre-processing algorithm, which can be used in conjunction with any matching algorithm to handle the so-called non-ideal iris images. In this thesis we present a few robust techniques to process the non-ideal iris images so as to give a segmented iris image to the matching algorithm. The motivation behind this work is to reduce the false reject rates of the current recognition systems and to reduce the intra-class variability. A new technique for estimating and compensating the angle in non-frontal iris images is presented. We have also developed a novel segmentation algorithm, which uses an ellipse-fitting approach for localizing the pupil. A fast and simple limbus boundary segmentation algorithm is also presented

Color space analysis for iris recognition

This thesis investigates issues related to the processing of multispectral and color infrared images of the iris. When utilizing the color bands of the electromagnetic spectrum, the eye color and the components of texture (luminosity and chromaticity) must be considered. This work examines the effect of eye color on texture-based iris recognition in both the near-IR and visible bands. A novel score level fusion algorithm for multispectral iris recognition is presented in this regard. The fusion algorithm - based on evidence that matching performance of a texture-based encoding scheme is impacted by the quality of texture within the original image - ranks the spectral bands of the image based on texture quality and designs a fusion rule based on these rankings. Color space analysis, to determine an optimal representation scheme, is also examined in this thesis. Color images are transformed from the sRGB color space to the CIE Lab, YCbCr, CMYK and HSV color spaces prior to encoding and matching. Also, enhancement methods to increase the contrast of the texture within the iris, without altering the chromaticity of the image, are discussed. Finally, cross-band matching is performed to illustrate the correlation between eye color and specific bands of the color image

LIMBUSTRACK: STABLE EYE-TRACKING IN IMPERFECT LIGHT CONDITIONS

We are aware of only one serious effort at development of a cheap, accurate, wearable eye tracker: the open source openEyes project. However, its method of ocular feature detection is such that it is prone to failure in variable lighting conditions. To address this deficiency, we have developed a cheap wearable eye tracker. At the heart of our development are novel techniques that allow operation under variable illumination