Fabrication of 3D Fingerprint Phantoms via Unconventional Polycarbonate Molding

Fingerprint biometrics is a valuable and convenient security tool; every fingerprint is highly detailed and unique, we always have them on “hand”. Herein we describe a novel bench-top method of making 3D fingerprint replicas (namely, fingerprint phantoms) by exploring a unique microfabrication approach using conventional polymeric materials, to aid the development of reliable and accurate fingerprint biometrics. By pressing an impression of human fingerprints onto solvent-softened plastic plates (e.g., polycarbonate chips), followed by casting with polydimethylsiloxane (PDMS, a popular elastomer), we can produce a flexible, nanoscale detailed, 3D reproduction of the fingerprint (“phantom”). By testing with standard optical fingerprint scanners, we have shown that all three levels of fingerprint details can be precisely recorded and match well with the original fingerprint. Superior to artificial fingerprint patterns, these phantoms have the exact 3D features of fingerprints and introduce no variability compared to human sampling, which make them perfect targets for standardizing fingerprint scanners and for biometric applications. We envision that the microcontact replication protocol via unconventional PC molding promises a practical, bench-top, instrumentation-free method to mass reproduce many other micro/nanostructures with high fidelity

Simulating the Effects of Skin Thickness and Fingerprints to Highlight Problems with Non-invasive RF Blood Glucose Sensing from Fingertips

The non-invasive measurement of blood glucose is a popular research topic where RF/microwave sensing of glucose is one of the promising methods in this area. From the many available measurement sites in the human body, fingertips appear to be a good choice due to a good amount of fresh blood supply and homogeneity in terms of biological layers present. The non-invasive RF measurement of blood glucose relies on the detection of the change in the permittivity of the blood using a resonator as a sensor. However, the change in the permittivity of blood due to the variation in glucose content has a limited range resulting in a very small shift in the sensor’s frequency response. Any inconsistency between measurements may hinder the measurement results. These inconsistencies mostly arise from the varied thickness of the biological layers and variation of fingerprints that are unique to every human. Therefore, the effects of biological layers and fingerprints in fingertips were studied in detail and are reported in this paper

Monolithic ultrasound fingerprint sensor.

This paper presents a 591×438-DPI ultrasonic fingerprint sensor. The sensor is based on a piezoelectric micromachined ultrasonic transducer (PMUT) array that is bonded at wafer-level to complementary metal oxide semiconductor (CMOS) signal processing electronics to produce a pulse-echo ultrasonic imager on a chip. To meet the 500-DPI standard for consumer fingerprint sensors, the PMUT pitch was reduced by approximately a factor of two relative to an earlier design. We conducted a systematic design study of the individual PMUT and array to achieve this scaling while maintaining a high fill-factor. The resulting 110×56-PMUT array, composed of 30×43-μm2 rectangular PMUTs, achieved a 51.7% fill-factor, three times greater than that of the previous design. Together with the custom CMOS ASIC, the sensor achieves 2 mV kPa-1 sensitivity, 15 kPa pressure output, 75 μm lateral resolution, and 150 μm axial resolution in a 4.6 mm×3.2 mm image. To the best of our knowledge, we have demonstrated the first MEMS ultrasonic fingerprint sensor capable of imaging epidermis and sub-surface layer fingerprints

3D printed realistic finger vein phantoms

Finger vein pattern recognition is an emerging biometric with a good resistance to presentation attacks and low error rates. One problem is that it is hard to obtain ground truth finger vein patterns from live fingers. In this paper we propose an advanced method to create finger vein phantoms using 3D printing where we mimic the optical properties of the various tissues inside the fingers, like bone, veins and soft tissues using different printing materials and parameters. We demonstrate that we are able to create finger phantoms that result in realistic finger vein images and precisely known vein patterns. These phantoms can be used to develop and evaluate finger vein extraction and recognition methods. In addition, we show that the finger vein phantoms can be used to spoof a finger vein recognition system. This paper is based on the Master's thesis of Rasmus van der Grift

Using Raman Spectroscopy for Intraoperative Margin Analysis in Breast Conserving Surgery

Breast Conserving Surgery (BCS) in the treatment of breast cancer aims to provide optimal oncological results, with minimal tissue excision to optimise cosmetic outcome. Positive margins due to an inadequate resection occurs in 17% of UK patients undergoing BCS and prompts recommendation for further tissue re-excision to reduce recurrence risk. A second operation causes patient anxiety and significant healthcare costs. This issue could be resolved with accurate intra-operative margin analysis (IMA) to enable excision of all cancerous tissue at the index procedure. High wavenumber Raman Spectroscopy (HWN RS) is a vibrational spectroscopy highly sensitive to changes in protein/lipid environment and water content –biochemical differences found between tumour and normal breast tissue. We proposed that HWN RS could be used to differentiate between tumour and non-tumour breast tissue with a view to future IMA. This thesis presents the development of a Raman system to measure the HWN region capable of accurately detecting changes in protein, lipid and water content, in the presence of highly fluorescent surgical pigments such as blue dye that are present in surgically excised specimens. We investigate the relationship between changes in the HWN spectra with changes in water content in constructed breast phantoms to mimic protein and lipid rich environments and biological tissue. Human breast tissue of paired tumour and non-tumour samples were then measured and analysed. We found that breast tumour tissue is a protein rich, high water, low fat environment and that non-tumour is a low protein, fat rich environment with a low water content, and this can be used to identify breast cancer using HWN RS with excellent accuracy of over 90%. This thesis demonstrates a HWN RS Raman system capable of differentiating between tumour and non-tumour tissue in human breast tissue, and this has the potential to provide IMA in BCS

Only-Train-Once MR Fingerprinting for Magnetization Transfer Contrast Quantification

Magnetization transfer contrast magnetic resonance fingerprinting (MTC-MRF) is a novel quantitative imaging technique that simultaneously measures several tissue parameters of semisolid macromolecule and free bulk water. In this study, we propose an Only-Train-Once MR fingerprinting (OTOM) framework that estimates the free bulk water and MTC tissue parameters from MR fingerprints regardless of MRF schedule, thereby avoiding time-consuming process such as generation of training dataset and network training according to each MRF schedule. A recurrent neural network is designed to cope with two types of variants of MRF schedules: 1) various lengths and 2) various patterns. Experiments on digital phantoms and in vivo data demonstrate that our approach can achieve accurate quantification for the water and MTC parameters with multiple MRF schedules. Moreover, the proposed method is in excellent agreement with the conventional deep learning and fitting methods. The flexible OTOM framework could be an efficient tissue quantification tool for various MRF protocols.Comment: Accepted at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'22