Ocular biometrics has made significant strides over the past decade primarily due to the rapid advances in iris recognition. Recent literature has investigated the possibility of using conjunctival vasculature as an added ocular biometric. These patterns, observed on the sclera of the human eye, are especially significant when the iris is off-angle with respect to the acquisition device resulting in the exposure of the scleral surface. In this work, we design enhancement and registration methods to process and match conjunctival vasculature obtained under non-ideal conditions. The goal is to determine if conjunctival vasculature is a viable biometric in an operational environment. Initial results are promising and suggest the need for designing advanced image processing and registration schemes for furthering the utility of this novel biometric. However, we postulate that in an operational environment, conjunctival vasculature has to be used with the iris in a bimodal configuration.
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 regionbased 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. ACKNOWLEDGMENTS I wish to express my gratitude to my advisor Dr. Arun Ross for his guidance, knowledge and invaluable assistance throughout my research work. He has made available his support whenever I needed it. I owe my deepest gratitude to my supervisor, Dr. Lawrence Hornak for his support during the duration of my studies. Special thank you to the members of the supervisory committee Dr. Donald Adjeroh, Dr. Xin Li and Dr. Odom Vernon that contributes to the success of this study. I would also like to thank my husband Musat, and my children, Irina and Tudor, for their love and support.
A new biometric indicator based on the patterns of conjunctival vasculature is proposed. Conjunctival vessels can be observed on the visible part of the sclera that is exposed to the outside world. These vessels demonstrate rich and specific details in visible light, and can be easily photographed using a regular digital camera. In this paper we discuss methods for conjunctival imaging, preprocessing, and feature extraction in order to derive a suitable conjunctival vascular template for biometric authentication. Commensurate classification methods along with the observed accuracy are discussed. Experimental results suggest the potential of using conjunctival vasculature as a biometric measure.
Abstract-A recent report from the National Institute of Standards and Technology (NIST) showed that changes in pupil dilation affect the performance of iris recognition algorithms. Hence, there is a need to explore the effects of pupil motion from a biological standpoint. Our work looks at the pupil's response to light, otherwise known as the pupil light reflex (PLR). By modeling the PLR using a nonlinear delay differential equation while considering images acquired in the near infrared (NIR) spectral band, we study both average and subject-specific pupil dilation effects. Experiments conducted on the WVU iris video dataset 1 convey the efficacy of our work in describing and evaluating pupillary response for both general and individual responses. The results of this work can be used to develop robust iris recognition algorithms that handle the effects of pupil dilation.
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 regionbased 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. ACKNOWLEDGMENTS I wish to express my gratitude to my advisor Dr. Arun Ross for his guidance, knowledge and invaluable assistance throughout my research work. He has made available his support whenever I needed it. I owe my deepest gratitude to my supervisor, Dr. Lawrence Hornak for his support during the duration of my studies. Special thank you to the members of the supervisory committee Dr. Donald Adjeroh, Dr. Xin Li and Dr. Odom Vernon that contributes to the success of this study. I would also like to thank my husband Musat, and my children, Irina and Tudor, for their love and support.
Representative ways to analyze and survey changes in long-term electrocardiographic recordings Simona Gabriela Crihalmeanu The goal of this research is to explore techniques with which long-term physiologic timeseries data can be analyzed, so that relevant changes in physiological signals, particularly the electrocardiogram signal, can be captured, processed, quantified and stored. A new experimental model was developed such that the electrocardiogram can be monitored continuously over thirteen weeks. Cardiotoxicity was progressively induced with doxorubicin in a rabbit model, and electrocardiographic progressions from normal state to diseased state were continuously tracked. Automated methods for analyzing the data were developed to manage and control the extensive electrocardiogram dataset. A significant challenge to this work is the sheer mass of data. This experiment generated 180 megabytes per day per rabbit, totaling around 66 gigabytes for the entire study. Classical ECG parameters significant for the evaluation of heart rate variability were calculated by computer for the entire period of the recordings, and visualized with six different methods.
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