{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/58686"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/58686","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Segmentation and Feature Extraction Methods for Ocular Modalities","abstract":"The demand for better and robust security is an undisputed part of our interconnected world. Biometrics based solutions offer an alternative to passwords and other form of authentications. Especially, Ocular biometrics has gained higher demand based on their proven recognition rates and reliable texture patterns over time. Physical and psycho-physiological aspects of human eye reveal unique signatures from physical appearance and cognitive state. Physical aspects of human eye include unique texture pattern from Iris, Retina and Sclera. Based on uniqueness, universality and availability, ocular modalities can be potentially be used as biometric token. Iris biometrics is considered as the optimal solution with given image acquisition in near infrared environment. Iris biometrics in visible spectrum (due to darker irises) and off-angle iris recognition is challenging. As an attempt to address the limitation from Iris recognition, we design a hyper focal imaging platform for visible spectrum iris recognition. Lateral lighting is used for illumination and hyper focal imaging technique is used to acquire super sharp iris image. In this study, visible spectrum iris recognition is successfully performed but the proposed setup limits its applications to cooperative users and requires additional light source. Usage of conjunctival vasculature (CV) recognition is one of the alternative solutions to overcome limitations from iris recognition. Red blood vessel pattern seen on white of eye is termed as conjunctival vasculature. CV can be potentially used as biometric modality due to its uniqueness, availability in visible spectrum and its maximum exposure in gazing. In this dissertation, we design and evaluate the scleral segmentation algorithms, vascular pattern enhancement techniques, feature extraction methodologies and feature enhancement methods for CV recognition. We introduce a new sclera segmentation algorithm using morphological properties of human eye in visible spectrum. This study demonstrates the effective use of the multi-scale multi-directional transform (MSMDT) for feature extraction from CV. Non-linear feature enhancement and feature mapping in various MSMDT domain are successfully demonstrated for the differentiation of texture. Psycho-Physiological aspects of human eye reveal cognitive state of human brain. In this dissertation, startle eye blink signals are used for assessment of credibility from 250 FPS video records. Using upper eyelid tracking at 250 FPS and ensemble of SVMs, we were able to detect deceptive individuals with sensitivity, specificity, correct rate of up to 0.7857, 1, and 0.8636 (5-fold cross validation).","abstract_html":"The demand for better and robust security is an undisputed part of our interconnected world. Biometrics based solutions offer an alternative to passwords and other form of authentications. Especially, Ocular biometrics has gained higher demand based on their proven recognition rates and reliable texture patterns over time. Physical and psycho-physiological aspects of human eye reveal unique signatures from physical appearance and cognitive state. Physical aspects of human eye include unique texture pattern from Iris, Retina and Sclera. Based on uniqueness, universality and availability, ocular modalities can be potentially be used as biometric token. Iris biometrics is considered as the optimal solution with given image acquisition in near infrared environment. Iris biometrics in visible spectrum (due to darker irises) and off-angle iris recognition is challenging. As an attempt to address the limitation from Iris recognition, we design a hyper focal imaging platform for visible spectrum iris recognition. Lateral lighting is used for illumination and hyper focal imaging technique is used to acquire super sharp iris image. In this study, visible spectrum iris recognition is successfully performed but the proposed setup limits its applications to cooperative users and requires additional light source. Usage of conjunctival vasculature (CV) recognition is one of the alternative solutions to overcome limitations from iris recognition. Red blood vessel pattern seen on white of eye is termed as conjunctival vasculature. CV can be potentially used as biometric modality due to its uniqueness, availability in visible spectrum and its maximum exposure in gazing. In this dissertation, we design and evaluate the scleral segmentation algorithms, vascular pattern enhancement techniques, feature extraction methodologies and feature enhancement methods for CV recognition. We introduce a new sclera segmentation algorithm using morphological properties of human eye in visible spectrum. This study demonstrates the effective use of the multi-scale multi-directional transform (MSMDT) for feature extraction from CV. Non-linear feature enhancement and feature mapping in various MSMDT domain are successfully demonstrated for the differentiation of texture. Psycho-Physiological aspects of human eye reveal cognitive state of human brain. In this dissertation, startle eye blink signals are used for assessment of credibility from 250 FPS video records. Using upper eyelid tracking at 250 FPS and ensemble of SVMs, we were able to detect deceptive individuals with sensitivity, specificity, correct rate of up to 0.7857, 1, and 0.8636 (5-fold cross validation).","abstract_has_math":false,"creators":["Tankasala, Sriram Pavan"],"institution":"University of Missouri--Kansas City","degree_name":"Ph.D. (Doctor of Philosophy)","degree_level":"Doctoral","degree_discipline":"Electrical and Computer Engineering (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Derakhshani, Reza"],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-24T05:16:57Z","subjects":[],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/58686","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Derakhshani, Reza"]},{"key":"dc:creator","label":"Author","values":["Tankasala, Sriram Pavan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-01-25T18:47:53Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-01-25T18:47:53Z"]},{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Kansas City"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering (UMKC)","Telecommunications and Computer Networking (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D. (Doctor of Philosophy)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/58686"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page viewed June 28, 2024","Dissertation advisor: Reza Derakhshani","Vita","Includes bibliographical references (pages 160-170)","Dissertation (Ph.D.)--School of Computing and Engineering. 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As an attempt to address the limitation from Iris recognition, we design a hyper focal imaging platform for visible spectrum iris recognition. Lateral lighting is used for illumination and hyper focal imaging technique is used to acquire super sharp iris image. In this study, visible spectrum iris recognition is successfully performed but the proposed setup limits its applications to cooperative users and requires additional light source. Usage of conjunctival vasculature (CV) recognition is one of the alternative solutions to overcome limitations from iris recognition. Red blood vessel pattern seen on white of eye is termed as conjunctival vasculature. CV can be potentially used as biometric modality due to its uniqueness, availability in visible spectrum and its maximum exposure in gazing. In this dissertation, we design and evaluate the scleral segmentation algorithms, vascular pattern enhancement techniques, feature extraction methodologies and feature enhancement methods for CV recognition. We introduce a new sclera segmentation algorithm using morphological properties of human eye in visible spectrum. This study demonstrates the effective use of the multi-scale multi-directional transform (MSMDT) for feature extraction from CV. Non-linear feature enhancement and feature mapping in various MSMDT domain are successfully demonstrated for the differentiation of texture. Psycho-Physiological aspects of human eye reveal cognitive state of human brain. In this dissertation, startle eye blink signals are used for assessment of credibility from 250 FPS video records. Using upper eyelid tracking at 250 FPS and ensemble of SVMs, we were able to detect deceptive individuals with sensitivity, specificity, correct rate of up to 0.7857, 1, and 0.8636 (5-fold cross validation)."]},{"key":"dc:title","label":"Title","values":["Segmentation and Feature Extraction Methods for Ocular Modalities"]}]}],"canonical_facts":{"dc:contributor.advisor":["Derakhshani, Reza"],"dc:creator":["Tankasala, Sriram Pavan"],"dc:date.accessioned":["2017-01-25T18:47:53Z"],"dc:date.available":["2017-01-25T18:47:53Z"],"dc:date.issued":["2015"],"dc:description":["Title from PDF of title page viewed June 28, 2024","Dissertation advisor: Reza Derakhshani","Vita","Includes bibliographical references (pages 160-170)","Dissertation (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015"],"dc:description.abstract":["The demand for better and robust security is an undisputed part of our interconnected world. Biometrics based solutions offer an alternative to passwords and other form of authentications. Especially, Ocular biometrics has gained higher demand based on their proven recognition rates and reliable texture patterns over time. Physical and psycho-physiological aspects of human eye reveal unique signatures from physical appearance and cognitive state. Physical aspects of human eye include unique texture pattern from Iris, Retina and Sclera. Based on uniqueness, universality and availability, ocular modalities can be potentially be used as biometric token. Iris biometrics is considered as the optimal solution with given image acquisition in near infrared environment. Iris biometrics in visible spectrum (due to darker irises) and off-angle iris recognition is challenging. As an attempt to address the limitation from Iris recognition, we design a hyper focal imaging platform for visible spectrum iris recognition. Lateral lighting is used for illumination and hyper focal imaging technique is used to acquire super sharp iris image. In this study, visible spectrum iris recognition is successfully performed but the proposed setup limits its applications to cooperative users and requires additional light source. Usage of conjunctival vasculature (CV) recognition is one of the alternative solutions to overcome limitations from iris recognition. Red blood vessel pattern seen on white of eye is termed as conjunctival vasculature. CV can be potentially used as biometric modality due to its uniqueness, availability in visible spectrum and its maximum exposure in gazing. In this dissertation, we design and evaluate the scleral segmentation algorithms, vascular pattern enhancement techniques, feature extraction methodologies and feature enhancement methods for CV recognition. We introduce a new sclera segmentation algorithm using morphological properties of human eye in visible spectrum. This study demonstrates the effective use of the multi-scale multi-directional transform (MSMDT) for feature extraction from CV. Non-linear feature enhancement and feature mapping in various MSMDT domain are successfully demonstrated for the differentiation of texture. Psycho-Physiological aspects of human eye reveal cognitive state of human brain. In this dissertation, startle eye blink signals are used for assessment of credibility from 250 FPS video records. Using upper eyelid tracking at 250 FPS and ensemble of SVMs, we were able to detect deceptive individuals with sensitivity, specificity, correct rate of up to 0.7857, 1, and 0.8636 (5-fold cross validation)."],"dc:identifier.uri":["https://hdl.handle.net/10355/58686"],"dc:language.iso":["en_US"],"dc:publisher":["University of Missouri--Kansas City"],"dc:title":["Segmentation and Feature Extraction Methods for Ocular Modalities"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering (UMKC)","Telecommunications and Computer Networking (UMKC)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D. (Doctor of Philosophy)"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:16:57Z"}