A 1D spectral image validationverification metric for fingerprints

Libert, John M.; Grantham, John D.; Orandi, Shahram

doi:10.6028/nist.ir.7599

Cited by 11 publications

(20 citation statements)

References 12 publications

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“…The sensor has a resolution of 508 pixels per inch (ppi)—the FBI standard for fingerprint authentication . It comprises 640 × 480 pixels (resulting in a total sensor size of 3.2 × 2.4 cm).…”

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confidence: 99%

A High‐Resolution Thin‐Film Fingerprint Sensor Using a Printed Organic Photodetector

Tordera

Peeters

Akkerman

et al. 2019

Adv Materials Technologies

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However, it is not easy to process largearea, homogeneous thin OPD films by conventional coating techniques, such as spin-coating. Using slot-die coating of the active organic photodetector materials, we have developed a cost-effective, scalable photodetector array technology. We demonstrate its use in a high-resolution fingerprint detector with a thin form factor that ultimately would allow users to securely unlock their phone or open user-specific apps by touching anywhere on the display screen.The fingerprint scanner is based on three main building blocks (Figure 1a for a schematic of the fingerprint scanner, Figure 1b for the circuit diagram, and Figure S1, Supporting Information, for a full stack cross section). The backplane is a dual-gate self-aligned oxide thin-film transistor (DG-SA TFT) array, described and characterized elsewhere. [12] The second (bottom) gate is typically used to increase the transistor current for a given geometry and voltage. Here, it also acts as a light shield of the oxide semiconductor in case of backside exposure, thereby increasing device stability. [13] The DG-SA TFT exhibits a low off-current with values below 1 pA, lower than the off-currents of amorphous and low-temperature polycrystalline silicon transistors. This is due to the large bandgap of IGZO (≈3 eV) that results in a low leakage current. The field-effect mobility of our TFTs is 15 and 16 cm 2 V −1 s −1 for the top and bottom gate operation, respectively. In so-called dual gate mode, that is, V g top = V g bottom , the effective mobility is equivalent to 35 cm 2 V −1 s −1 . The values can be compared to a-Si TFTs (≈1 cm 2 V −1 s −1 ) and LTPS TFTs (≈100 cm 2 V −1 s −1 ). The frontplane consists of organic photodiodes (OPDs). The technology is based on a bulk heterojunction (BHJ) structure of a p-type (donor) polymer, polyPCDTBT, and an n-type (acceptor) fullerene, 6,6-phenyl-C61-butyric acid methyl ester, PCBM. The blend is deposited by slot-die coating, resulting in a 280 nm film. Semitransparent IGZO (bottom) and MoOx/Ag (top) electrodes are used. The a-IGZO is deposited on top of an opaque MoCr layer. This is done to prevent direct detection from the backlight, so only light that is reflected from objects on top of the imager array is measured by the OPD. The sensor is coupled with an optically transparent thin-film encapsulation to avoid moisture ingress that degrades the OPD materials. [14] The fabrication processes are compatible with flat panel display manufacturing lines. Details on the Organic photodetectors (OPDs) have attracted much attention in recent years, due to their promise in large-area light sensing applications. Here, high-resolution slot-die-coated large-area bulk heterojunction organic photodiode (OPD) arrays are reported. The OPD uses a novel electron transport layer, indium gallium zinc oxide in combination with a molybdenum oxide top-electrode. Together, these effectively reduce dark current densities to very low levels of ≈10 −7 mA cm −2 at −2 V. The OPDs show linear behavior in a wide rang...

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A High‐Resolution Thin‐Film Fingerprint Sensor Using a Printed Organic Photodetector

Tordera

Peeters

Akkerman

et al. 2019

Adv Materials Technologies

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“…Developed initially as a method to screen fingerprint databases for non-fingerprint images, segmentation errors, or mislabeled sample rates, the Spectral Image Validation Verification (SIVV) metric [6] provides a comparatively straightforward method by which to assess the frequency structure of a fingerprint image. Pairwise display of the SIVV signals of a pair of images enables summary visualization of the effects of differences across the composition frequency spectrum of the image.…”

Section: Frequency Spectrum Comparison 441 Nist Spectral Image Valimentioning

confidence: 99%

Contactless fingerprint devices usability test

Furman¹,

Stanton²,

Theofanos³

et al. 2017

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“…The relative height and frequency of the dominant spectral peak differentiates a fingerprint image from a variety of other image types, including other biometrics such as face and iris images, blank image frames as with live-scan "failure to acquire" errors, and faulty segmentation of individual prints from composite fingerprint cards or from multi-finger images. In view of its implementation and application the method bears the name, Spectral Image Validation and Verification (SIVV) utility (see [1] for detailed description.) In addition to its capacity for classification of fingerprint vs. non-fingerprint, the SIVV utility provides an estimate of scan sample rate of images, enabling detection of fingerprint specimens mislabeled with regard to pixel density.…”

Section: Overviewmentioning

confidence: 99%

“…Fingerprint centering, cropping, and windowing are applied first as described in[1] Proc. of SPIE Vol.…”

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Tools for quality control of fingerprint databases

Swann

Libert²,

Lepley

2010

SPIE Proceedings

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Integrity of fingerprint data is essential to biometric and forensic applications. Accordingly, the FBI's Criminal Justice Information Services (CJIS) Division has sponsored development of software tools to facilitate quality control functions relative to maintaining its fingerprint data assets inherent to the Integrated Automated Fingerprint Identification System (IAFIS) and Next Generation Identification (NGI). This paper provides an introduction of two such tools. The first FBIsponsored tool was developed by the National Institute of Standards and Technology (NIST) and examines and detects the spectral signature of the ridge-flow structure characteristic of friction ridge skin. The Spectral Image Validation/Verification (SIVV) utility differentiates fingerprints from non-fingerprints, including blank frames or segmentation failures erroneously included in data; provides a "first look" at image quality; and can identify anomalies in sample rates of scanned images. The SIVV utility might detect errors in individual 10-print fingerprints inaccurately segmented from the flat, multi-finger image acquired by one of the automated collection systems increasing in availability and usage. In such cases, the lost fingerprint can be recovered by re-segmentation from the now compressed multi-finger image record. The second FBI-sponsored tool, CropCoeff was developed by MITRE and thoroughly tested via NIST. CropCoeff enables cropping of the replacement single print directly from the compressed data file, thus avoiding decompression and recompression of images that might degrade fingerprint features necessary for matching.

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A 1D spectral image validationverification metric for fingerprints

Cited by 11 publications

References 12 publications

A High‐Resolution Thin‐Film Fingerprint Sensor Using a Printed Organic Photodetector

A High‐Resolution Thin‐Film Fingerprint Sensor Using a Printed Organic Photodetector

Contactless fingerprint devices usability test

Tools for quality control of fingerprint databases

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