Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli
A Master of Science thesis in Computer Engineering by Ali Alhaj Darwish entitled, "Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli," submitted in June 2013. Thesis advisor is Dr. Michel Pasquier. Available are both soft and ha...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | doctoralThesis |
| منشور في: |
2013
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/5898 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513444515938304 |
|---|---|
| author | Darwish, Ali Alhaj |
| author_facet | Darwish, Ali Alhaj |
| author_role | author |
| dc.contributor.none.fl_str_mv | Pasquier, Michel |
| dc.creator.none.fl_str_mv | Darwish, Ali Alhaj |
| dc.date.none.fl_str_mv | 2013-09-11T06:42:23Z 2013-09-11T06:42:23Z 2013-06 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2013.25 http://hdl.handle.net/11073/5898 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | behavioral biometrics eye-movement biometrics iris biometrics non-intrusive identification task-independent identification stealth identification machine learning Biometric identification Eye |
| dc.title.none.fl_str_mv | Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Computer Engineering by Ali Alhaj Darwish entitled, "Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli," submitted in June 2013. Thesis advisor is Dr. Michel Pasquier. Available are both soft and hard copies of the thesis. |
| format | doctoralThesis |
| id | aus_fe12a279c7acebdcecddda36ce751b95 |
| identifier_str_mv | 35.232-2013.25 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/5898 |
| publishDate | 2013 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent StimuliDarwish, Ali Alhajbehavioral biometricseye-movement biometricsiris biometricsnon-intrusive identificationtask-independent identificationstealth identificationmachine learningBiometric identificationEyeA Master of Science thesis in Computer Engineering by Ali Alhaj Darwish entitled, "Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli," submitted in June 2013. Thesis advisor is Dr. Michel Pasquier. Available are both soft and hard copies of the thesis.This work investigates the feasibility of using the dynamic features of the eyes for biometric identification. Identifying individuals using eye movements is typically limited by a low accuracy, thus preventing this technique from becoming commercially viable. In addition, the human eyes constitute a rich source of information, still only partially understood so far, hence more research is needed to understand exactly what kind of information they can provide, and what technique should be applied to analyze such information. It is also largely unknown what kind of feature will yield accurate data most useful to biometric identification, or which stimuli most influence most the dynamic features of the eyes and their usability as a biometrical trait. We show that, by combining eye movement features and iris constriction and dilation parameters, the dynamic features of the eye can yield a good level of accuracy for biometric systems. The approach consists of recording and categorizing eye movements as well as changes in pupil size into segments consisting of saccades and fixations, and computing for each the many velocity and acceleration features that are used to train the classifier to perform the biometric identification. We tested four types of stimuli to hypothesize which will provide a viable stimulating method for extracting eye features. The results suggest that simple stimuli such as images and graphs can appropriately excite the dynamic features of the eye for the purpose of biometric identification.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Computer Engineering (MSCoE)Pasquier, Michel2013-09-11T06:42:23Z2013-09-11T06:42:23Z2013-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2013.25http://hdl.handle.net/11073/5898en_USoai:repository.aus.edu:11073/58982025-06-26T12:35:02Z |
| spellingShingle | Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli Darwish, Ali Alhaj behavioral biometrics eye-movement biometrics iris biometrics non-intrusive identification task-independent identification stealth identification machine learning Biometric identification Eye |
| status_str | publishedVersion |
| title | Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli |
| title_full | Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli |
| title_fullStr | Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli |
| title_full_unstemmed | Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli |
| title_short | Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli |
| title_sort | Biometric Identification Based on Eye Movement and Iris Features Using Task-Driven and Task-Independent Stimuli |
| topic | behavioral biometrics eye-movement biometrics iris biometrics non-intrusive identification task-independent identification stealth identification machine learning Biometric identification Eye |
| url | http://hdl.handle.net/11073/5898 |