Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline

 

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dc.contributor.advisor Marais, Patrick
dc.contributor.author Pitcher, Courtney Richard
dc.date.accessioned 2021-09-15T15:19:58Z
dc.date.available 2021-09-15T15:19:58Z
dc.date.issued 2021_
dc.identifier.citation Pitcher, C.R. 2021. Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline. . ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/33923 en_ZA
dc.identifier.uri http://hdl.handle.net/11427/33923
dc.description.abstract Identifying people from their fingerprints is based on well established technology. However, a number of challenges remain, notably overcoming the low feature density of the surface fingerprint and suboptimal feature matching. 2D contact based fingerprint scanners offer low security performance, are easy to spoof, and are unhygienic. Optical Coherence Tomography (OCT) is an emerging technology that allows a 3D volumetric scan of the finger surface and its internal microstructures. The junction between the epidermis and dermis - the internal fingerprint - mirrors the external fingerprint. The external fingerprint is prone to degradation from wear, age, or disease. The internal fingerprint does not suffer these deficiencies, which makes it a viable candidate zone for feature extraction. We develop a biometrics pipeline that extracts and matches features from and around the internal fingerprint to address the deficiencies of contemporary 2D fingerprinting. Eleven different feature types are explored. For each type an extractor and Iterative Closest Point (ICP) matcher is developed. ICP is modified to operate in a Cartesiantoroidal space. Each of these features are matched with ICP against another matcher, if one existed. The feature that has the highest Area Under the Curve (AUC) on an Receiver Operating Characteristic of 0.910 is a composite of 3D minutia and mean local cloud, followed by our geometric properties feature, with an AUC of 0.896. By contrast, 2D minutiae extracted from the internal fingerprint achieved an AUC 0.860. These results make our pipeline useful in both access control and identification applications. ICP offers a low False Positive Rate and can match ∼30 composite 3D minutiae a second on a single threaded system, which is ideal for access control. Identification systems require a high True Positive and True Negative Rate, in addition time is a less stringent requirement. New identification systems would benefit from the introduction of an OCT based pipeline, as all the 3D features we tested provide more accurate matching than 2D minutiae. We also demonstrate that ICP is a viable alternative to match traditional 2D features (minutiae). This method offers a significant improvement over the popular Bozorth3 matcher, with an AUC of 0.94 for ICP versus 0.86 for Bozorth3 when matching a highly distorted dataset generated with SFinGe. This compatibility means that ICP can easily replace other matchers in existing systems to increase security performance.
dc.subject Computer Science
dc.title Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline
dc.type Master Thesis
dc.date.updated 2021-09-15T02:25:03Z
dc.language.rfc3066 eng
dc.publisher.faculty Faculty of Science
dc.publisher.department Department of Computer Science
dc.type.qualificationlevel Masters
dc.type.qualificationlevel MSc
dc.identifier.apacitation Pitcher, C. R. (2021). <i>Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline</i>. (). ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/33923 en_ZA
dc.identifier.chicagocitation Pitcher, Courtney Richard. <i>"Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline."</i> ., ,Faculty of Science ,Department of Computer Science, 2021. http://hdl.handle.net/11427/33923 en_ZA
dc.identifier.vancouvercitation Pitcher CR. Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline. []. ,Faculty of Science ,Department of Computer Science, 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/33923 en_ZA
dc.identifier.ris TY - Master Thesis AU - Pitcher, Courtney Richard AB - Identifying people from their fingerprints is based on well established technology. However, a number of challenges remain, notably overcoming the low feature density of the surface fingerprint and suboptimal feature matching. 2D contact based fingerprint scanners offer low security performance, are easy to spoof, and are unhygienic. Optical Coherence Tomography (OCT) is an emerging technology that allows a 3D volumetric scan of the finger surface and its internal microstructures. The junction between the epidermis and dermis - the internal fingerprint - mirrors the external fingerprint. The external fingerprint is prone to degradation from wear, age, or disease. The internal fingerprint does not suffer these deficiencies, which makes it a viable candidate zone for feature extraction. We develop a biometrics pipeline that extracts and matches features from and around the internal fingerprint to address the deficiencies of contemporary 2D fingerprinting. Eleven different feature types are explored. For each type an extractor and Iterative Closest Point (ICP) matcher is developed. ICP is modified to operate in a Cartesiantoroidal space. Each of these features are matched with ICP against another matcher, if one existed. The feature that has the highest Area Under the Curve (AUC) on an Receiver Operating Characteristic of 0.910 is a composite of 3D minutia and mean local cloud, followed by our geometric properties feature, with an AUC of 0.896. By contrast, 2D minutiae extracted from the internal fingerprint achieved an AUC 0.860. These results make our pipeline useful in both access control and identification applications. ICP offers a low False Positive Rate and can match ∼30 composite 3D minutiae a second on a single threaded system, which is ideal for access control. Identification systems require a high True Positive and True Negative Rate, in addition time is a less stringent requirement. New identification systems would benefit from the introduction of an OCT based pipeline, as all the 3D features we tested provide more accurate matching than 2D minutiae. We also demonstrate that ICP is a viable alternative to match traditional 2D features (minutiae). This method offers a significant improvement over the popular Bozorth3 matcher, with an AUC of 0.94 for ICP versus 0.86 for Bozorth3 when matching a highly distorted dataset generated with SFinGe. This compatibility means that ICP can easily replace other matchers in existing systems to increase security performance. DA - 2021_ DB - OpenUCT DP - University of Cape Town KW - Computer Science LK - https://open.uct.ac.za PY - 2021 T1 - Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline TI - Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline UR - http://hdl.handle.net/11427/33923 ER - en_ZA


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