Novelty Detection‐Based Internal Fingerprint Segmentation in Optical Coherence Tomography Images

Biometric fingerprint scanners scan the external skin\u27s 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\u27s 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 modified Williams index above 0.9400

Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey

The vulnerabilities of fingerprint authentication systems have raised security concerns when adapting them to highly secure access-control applications. Therefore, Fingerprint Presentation Attack Detection (FPAD) methods are essential for ensuring reliable fingerprint authentication. Owing to the lack of generation capacity of traditional handcrafted based approaches, deep learning-based FPAD has become mainstream and has achieved remarkable performance in the past decade. Existing reviews have focused more on hand-cratfed rather than deep learning-based methods, which are outdated. To stimulate future research, we will concentrate only on recent deep-learning-based FPAD methods. In this paper, we first briefly introduce the most common Presentation Attack Instruments (PAIs) and publicly available fingerprint Presentation Attack (PA) datasets. We then describe the existing deep-learning FPAD by categorizing them into contact, contactless, and smartphone-based approaches. Finally, we conclude the paper by discussing the open challenges at the current stage and emphasizing the potential future perspective.Comment: 29 pages, submitted to ACM computing survey journa

Internal fingerprint extraction

Fingerprints are a non-invasive biometric that possess significant advantages. However, they are subject to surface erosion and damage; distortion upon scanning; and are vulnerable to fingerprint spoofing. The internal fingerprint exists as the undulations of the papillary junction - an intermediary layer of skin - and provides a solution to these disadvantages. Optical coherence tomography is used to capture the internal fingerprint. A depth profile of the papillary junction throughout the OCT scans is first constructed using fuzzy c-means clustering and a fine-tuning procedure. This information is then used to define localised regions over which to average pixels for the resultant internal fingerprint. When compared to a ground-truth internal fingerprint zone, the internal fingerprint zone detected automatically is within the measured bounds of human error. With a mean- squared-error of 21.3 and structural similarity of 96.4%, the internal fingerprint zone was successfully found and described. The extracted fingerprints exceed their surface counterparts with respect to orientation certainty and NFIQ scores (both of which are respected fingerprint quality assessment criteria). Internal to surface fingerprint correspondence and internal fingerprint cross correspondence were also measured. A larger scanned region is shown to be advantageous as internal fingerprints extracted from these scans have good surface correspondence (75% had at least one true match with a surface counterpart). It is also evidenced that internal fingerprints can constitute a fingerprint database. 96% of the internal fingerprints extracted had at least one corresponding match with another internal fingerprint. When compared to surface fingerprints cropped to match the internal fingerprints’ representative area and locality, the internal fingerprints outperformed these cropped surface counterparts. The internal fingerprint is an attractive biometric solution. This research develops a novel approach to extracting the internal fingerprint and is an asset to the further development of technologies surrounding fingerprint extraction from OCT scans. No earlier work has extracted or tested the internal fingerprint to the degree that this research has