Khutlang, RethabileMachaka, PheehaSingh, AnnNelwamondo, Fulufhelo V2017-08-292017-08-292017-08Khutlang, R., Machaka, P., Singh, A. et al. 2017. Novelty detection-based internal fingerprint segmentation in optical coherence tomography images. In: Computed Tomography - Advanced Applications, Intech, p. 189-206. doi.org/10.5772/67594978-953-51-3368-1https://www.intechopen.com/books/computed-tomography-advanced-applicationsdoi.org/10.5772/67594http://hdl.handle.net/10204/9504Copyright: 2017 The Authors. Published under a Creative Commons License.Biometric fingerprint scanners scan the external skin's features onto a 2-D image. The performance of the automatic fingerprint identification system suffers first and foremost if the finger skin is wet, worn out or a fake fingerprint is used. We present an automatic segmentation of the papillary layer method, from images acquired using contact-less 3-D swept source optical coherence tomography (OCT). The papillary contour represents the internal fingerprint, which does not suffer from the external finger problems. It is embedded between the upper epidermis and papillary layers. Speckle noise is first reduced using non-linear filters from the slices composing the 3-D image. Subsequently, the stratum corneum is used to extract the epidermis. The epidermis, with its depth known, is used as the target class of the ensuing novelty detection. The outliers resulting from novelty detection represent the papillary layer. The contour of the papillary layer is segmented as the boundary between target and rejection classes. Using a mixture of Gaussian's novelty detection routine on images pre-processed with a regularized anisotropic diffusion filter, the papillary contours—internal fingerprints—are consistent with those segmented manually, with the modifiedWilliams index above 0.9400.enBiometricsNovelty detectionSegmentationInternal fingerprintOptical coherence tomographyOCTBook ChapterKhutlang, R., Machaka, P., Singh, A., & Nelwamondo, F. V. (2017). Novelty detection-Based internal fingerprint segmentation in optical coherence tomography images., <i>Worklist;19415</i> Intech. http://hdl.handle.net/10204/9504Khutlang, Rethabile, Pheeha Machaka, Ann Singh, and Fulufhelo V Nelwamondo. "Novelty detection-based internal fingerprint segmentation in optical coherence tomography images" In <i>WORKLIST;19415</i>, n.p.: Intech. 2017. http://hdl.handle.net/10204/9504.Khutlang R, Machaka P, Singh A, Nelwamondo FV. Novelty detection-based internal fingerprint segmentation in optical coherence tomography images.. Worklist;19415. [place unknown]: Intech; 2017. [cited yyyy month dd]. http://hdl.handle.net/10204/9504.TY - Book Chapter AU - Khutlang, Rethabile AU - Machaka, Pheeha AU - Singh, Ann AU - Nelwamondo, Fulufhelo V AB - Biometric fingerprint scanners scan the external skin's features onto a 2-D image. The performance of the automatic fingerprint identification system suffers first and foremost if the finger skin is wet, worn out or a fake fingerprint is used. We present an automatic segmentation of the papillary layer method, from images acquired using contact-less 3-D swept source optical coherence tomography (OCT). The papillary contour represents the internal fingerprint, which does not suffer from the external finger problems. It is embedded between the upper epidermis and papillary layers. Speckle noise is first reduced using non-linear filters from the slices composing the 3-D image. Subsequently, the stratum corneum is used to extract the epidermis. The epidermis, with its depth known, is used as the target class of the ensuing novelty detection. The outliers resulting from novelty detection represent the papillary layer. The contour of the papillary layer is segmented as the boundary between target and rejection classes. Using a mixture of Gaussian's novelty detection routine on images pre-processed with a regularized anisotropic diffusion filter, the papillary contours—internal fingerprints—are consistent with those segmented manually, with the modifiedWilliams index above 0.9400. DA - 2017-08 DB - ResearchSpace DP - CSIR KW - Biometrics KW - Novelty detection KW - Segmentation KW - Internal fingerprint KW - Optical coherence tomography KW - OCT LK - https://researchspace.csir.co.za PY - 2017 SM - 978-953-51-3368-1 T1 - Novelty detection-based internal fingerprint segmentation in optical coherence tomography images TI - Novelty detection-based internal fingerprint segmentation in optical coherence tomography images UR - http://hdl.handle.net/10204/9504 ER -