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

Multi-Frame Superresolution Optical Coherence Tomography for High Lateral Resolution 3D Imaging

We report that high lateral resolution and high image quality optical coherence tomography (OCT) imaging can be achieved by the multi-frame superresolution technique. With serial sets of slightly lateral shifted low resolution C-scans, our multi-frame superresolution processing of these special sets at each depth layer can reconstruct a higher resolution and quality lateral image. Layer by layer repeat processing yields an overall high lateral resolution and quality 3D image. In theory, the superresolution with a subsequent deconvolution processing could break the diffraction limit as well as suppress the background noise. In experiment, about three times lateral resolution improvement has been verified from 24.8 to 7.81 μm and from 7.81 to 2.19 μm with the sample arm optics of 0.015 and 0.05 numerical apertures, respectively, as well as the image quality doubling in dB unit. The improved lateral resolution for 3D imaging of microstructures has been observed. We also demonstrated that the improved lateral resolution and image quality could further help various machine vision algorithms sensitive to resolution and noise. In combination with our previous work, an ultra-wide field-of-view and high resolution OCT has been implemented for static non-medical applications. For in vivo 3D OCT imaging, high quality 3D subsurface live fingerprint images have been obtained within a short scan time, showing beautiful and clear distribution of eccrine sweat glands and internal fingerprint layer, overcoming traditional 2D fingerprint reader and benefiting important biometric security applications

Optical Coherence Tomography and Its Non-medical Applications

Optical coherence tomography (OCT) is a promising non-invasive non-contact 3D imaging technique that can be used to evaluate and inspect material surfaces, multilayer polymer films, fiber coils, and coatings. OCT can be used for the examination of cultural heritage objects and 3D imaging of microstructures. With subsurface 3D fingerprint imaging capability, OCT could be a valuable tool for enhancing security in biometric applications. OCT can also be used for the evaluation of fastener flushness for improving aerodynamic performance of high-speed aircraft. More and more OCT non-medical applications are emerging. In this book, we present some recent advancements in OCT technology and non-medical applications

Gas Discharge Visualization: An Imaging and Modeling Tool for Medical Biometrics

The need for automated identification of a disease makes the issue of medical biometrics very current in our society. Not all biometric tools available provide real-time feedback. We introduce gas discharge visualization (GDV) technique as one of the biometric tools that have the potential to identify deviations from the normal functional state at early stages and in real time. GDV is a nonintrusive technique to capture the physiological and psychoemotional status of a person and the functional status of different organs and organ systems through the electrophotonic emissions of fingertips placed on the surface of an impulse analyzer. This paper first introduces biometrics and its different types and then specifically focuses on medical biometrics and the potential applications of GDV in medical biometrics. We also present our previous experience with GDV in the research regarding autism and the potential use of GDV in combination with computer science for the potential development of biological pattern/biomarker for different kinds of health abnormalities including cancer and mental diseases

Tactile sensing of shape : biomechanics of contact investigated using imaging and modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (leaves 123-131).The overall goal of this research effort is to improve the understanding of the biomechanics of skin as it pertains to human tactile sense. During touch, mechanoreceptors beneath the skin surface are mechanically loaded due to physical contact of the skin with an object and respond with a series of neural impulses. This neural population response is decoded by the central nervous system to result in tactile perception of properties such as the shape, surface texture and softness of the object. The particular approach taken in this research is to develop a realistic model of the human fingertip based on empirical measurements of in vivo geometric and material properties of skin layers, so that the mechanical response of the fingertip skin to different shapes of objects in contact can be investigated, to help identify the relevant mechanism that triggers the mechanoreceptors in tactile encoding of object shape. To obtain geometric data on the ridged skin surface and the layers underneath together with their deformation patterns, optical coherence tomography (OCT) was used to image human fingertips in vivo, free of load as well as when loaded with rigid indenters of different shapes.(cont.) The images of undeformed and deformed finger pads were obtained, processed, and used for biomechanically validating the fingertip model. To obtain material properties of skin layers, axial strain imaging using high frequency ultrasound backscatter microscopy (UBM) was utilized in experiments on human fingertips in vivo to estimate the ratio of stiffnesses of the epidermis and dermis. By utilizing the data from OCT and UBM experiments, a multilayered three dimensional finite element model of the human fingertip composed of the ridged fingerpad skin surface as well as the papillary interface between the epidermis and dermis was developed. The model was used to simulate static indentation of the fingertip by rigid objects of different shapes and to compute stress and strain measures, such as strain energy density (SED), and maximum compressive or tensile strain (MCS, MTS), which have been previously proposed as the relevant stimuli that trigger mechanoreceptor response.(cont.) The results showed that the intricate geometry of skin layers and inhomogeneous material properties around the locations of the SA-I and RA mechanoreceptors caused significant differences in the spatial distribution of candidate relevant stimuli, compared with other locations at the same depths or the predictions from previous homogeneous models of the fingertip. The distribution of the SED at the locations of SA-I mechanoreceptors and the distribution of MCS/MTS at the locations of RA mechanoreceptors under indentation of different object shapes were obtained to serve as predictions to be tested in future biomechanical and neurophysiological experiments.by Wan-Chen Wu.Ph.D

OCT en phase pour la reconnaissance biométrique par empreintes digitales et sa sécurisation

In an increasingly open world, the flows of people are brought to explode in the coming years. Facilitating, streamlining, and managing these flows, by maintaining strict security constraints, therefore represent a key element for the global socio-economic dynamism. This flows management is mainly based on knowledge and verification of person identity. For its practicality and a priori secured, biometrics, in particular fingerprints biometrics, has become an effective and unavoidable solution.Nevertheless, it still suffers from two severe limitations. The first one concerns the poor performances obtained with damaged fingers. This damage can be involuntary (e.g. manual workers) or volunteers, for purposes of anonymity. The second limitation consists in the vulnerability of the commonly used sensors. In particular, they are vulnerable to copies of stolen fingerprints, made by malicious persons for identity theft purpose. We believe that these limitations are due to the small amount of information brought by the usual biometric sensors. It often consists in a single print of the finger surface. However, the biological complexity of human tissue provides rich information, unique to each person, and very difficult to reproduce. We therefore proposed an imaging approach based on Optical Coherence Tomography (OCT), a 3D contactless optical sensor, to finely measure this information. The main idea of the thesis is therefore to explore novel ways to exploit this information in order to make biometrics more robust and truly secured. In particular, we have proposed and evaluated different fingerprint imaging methods, based on the phase of the OCT signalDans un monde de plus en plus ouvert, les flux de personnes sont amenés à exploser dans les prochaines années. Fluidifier et contrôler ces flux, tout en respectant de fortes contraintes sécuritaires, apparaît donc comme un élément clef pour favoriser le dynamisme économique mondial. Cette gestion des flux passe principalement par la connaissance et la vérification de l’identité des personnes. Pour son aspect pratique et a priori sécurisé, la biométrie, et en particulier celle des empreintes digitales, s’est imposée comme une solution efficace, et incontournable. Néanmoins, elle souffre de deux sévères limitations. La première concerne les mauvaises performances obtenues avec des doigts détériorés. Ces détériorations peuvent être involontaires (travailleurs manuels par exemple), ou bien volontaires, à des fins d’anonymisation. La deuxième concerne les failles de sécurité des capteurs. En particulier, ils sont vulnérables à des attaques avec de fausses empreintes, réalisées par des personnes mal intentionnées dans un but d’usurpation d’identité. D’après nous, ces limitations sont dues à la faible quantité d’information exploitée par les capteurs usuels. Elle se résume souvent à une simple image de la surface du doigt. Pourtant, la complexité biologique des tissus humains est telle qu’elle offre une information très riche, unique, et difficilement reproductible. Nous avons donc proposé une approche d’imagerie, basée sur la Tomographique par Cohérence Optique, un capteur 3D sans contact, permettant de mesurer finement cette information. L’idée majeure de la thèse consiste à étudier divers moyens de l’exploiter, afin de rendre la biométrie plus robuste et vraiment sécurisé

The Role of Fluorescence and Human Factors in Quantitative Transdermal Blood and Tissue Analysis Using NIR Raman Spectroscopy

This research is part of an ongoing project aimed at the application of combined near infrared (NIR) Raman and fluorescence spectroscopy to noninvasive in vivo blood analysis including but not limited to glucose monitoring. Coping with practicalities of human factors and exploring ways to obtain and use knowledge gained about autofluorescence to improve algorithms for blood and tissue analysis are the general goals of this research. Firstly, the study investigated the various sources of human factors pertinent to our concerns, such as fingerprints, turgor, skin hydration and pigmentation. We then introduced specialized in vivo apparatus including means for precise and reproducible placement of the tissues relative to the optical aperture, i.e., the position detector pressure monitor (PDPM). Based on solid instrumental performances, appropriate methodology is now provided for applying and maintaining pressure to keep surface tissues immobile during experiments while obtaining the desired blood content and flow. Secondly, in vivo human fingertip skin autofluorescence photobleaching under 200 mW 830 nm NIR irradiation is observed and it is characterized that: i) the majority of the photobleached fluorescence originates from static tissue not blood, ii) the bleaching (1/e point) occurs in 101-102 sec timescale, and also iii) a photobleached region remains bleached for at least 45 min but recovers completely within several hours. A corresponding extensive but not exhaustive in vitro systematic study narrowed down the major contributors of such fluorescence and bleaching to collagen, melanin, plasma and hemoglobin: two major static tissue constituents and two major blood proteins. Thirdly, we established that measuring the inelastic and elastic emissions simultaneously leads to a sensitive probe for volume changes of both red blood cells and plasma. An algorithm based on measurements obtained while performing research needed for this thesis, as well as some empirical calibration approaches, was presented. The calibrated algorithm showed real potential to track hematocrit variations in cardiac pulses, centrifugal loading, blood vessel blockage using tourniquet, and even during as subtle an occurrence as in a Valsalva maneuver. Finally, NIR fluorescence and photochemistry of pentosidine, a representative of the advanced glycation endproducts (AGEs) which accumulate with age and hyperglycemia, was studied. The results indicate that oxygen plays a pivotal role in its photobleaching process. We hypothesized and offered proofs showing that pentosidine is a 1O2 sensitizer that is also subject to attack by the 1O2 resulting in the photobleaching that is observed when probing tissue using NIR. The photobleaching reaction is kinetically first order in pentosidine and ground state oxygen, and in vivo effectively first order with NIR irradiation also

FedBiometric: Image Features Based Biometric Presentation Attack Detection Using Hybrid CNNs-SVM in Federated Learning

In the past few years, biometric identification systems have become popular for personal, national, and global security. In addition to other biometric modalities, facial and fingerprint recognition have gained popularity due to their uniqueness, stability, convenience, and cost-effectiveness compared to other biometric modalities. However, the evolution of fake biometrics, such as printed materials, 2D or 3D faces, makeup, and cosmetics, has brought new challenges. As a result of these modifications, several facial and fingerprint Presentation Attack Detection methods have been proposed to distinguish between live and spoof faces or fingerprints. Federated learning can play a significant role in this problem due to its distributed learning setting and privacy-preserving advantages. This work proposes a hybrid ResNet50-SVM based federated learning model for facial Presentation Attack Detection utilizing Local Binary Pattern (LBP), or Gabor filter-based extracted image features. For fingerprint Presentation Attack Detection (PAD), this work proposes a hybrid CNN-SVM based federated learning model utilizing Local Binary Pattern (LBP), or Histograms of Oriented Gradient (HOG)-based extracted image features