Fingerprint Vendor Technology Evaluation

Watson, Craig I.; Fiumara, Gregory; Tabassi, Elham; Cheng, Su; Flanagan, Patricia A.; Salamon, Wayne J.

doi:10.6028/nist.ir.8034

Cited by 38 publications

(30 citation statements)

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“…They are not interoperable across optical and capacitive readers as are the Universal Fingerprint Targets. 2 Best results taken from [3] & [4] 3 Results taken from [5] In summary, the 2D ground truth fingerprint features are found to be preserved during fabrication into a 3D universal fingerprint target and subsequent image acquisition (with high accuracy) by contact-based optical readers, contactlessoptical readers, and capacitive readers. In other words, the universal fingerprint target is highly interoperable across different fingerprint reader technologies commonly in use.…”

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confidence: 87%

“…As such, controlled, repeatable evaluations of the various components of fingerprint recognition systems must be performed. While past end-to-end evaluations such as FpVTE 2012 [2] have provided us with baseline statistics on the performance of state-of-the-art fingerprint recognition systems, much work remains to be done in developing rigorous evaluations of the reader 1 subcomponent of finger- 1. A distinction is made between fingerprint reader and fingerprint sensor. Fingerprint reader refers to the entire device and process, which captures your physical fingerprint and converts it into a digital image.…”

Section: Introductionmentioning

confidence: 99%

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Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations

Engelsma

Arora

Jain

et al. 2018

IEEE Trans.Inform.Forensic Secur.

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We present the design and manufacturing of high fidelity universal 3D fingerprint targets, which can be imaged on a variety of fingerprint sensing technologies, namely capacitive, contact-optical, and contactless-optical. Universal 3D fingerprint targets enable, for the first time, not only a repeatable and controlled evaluation of fingerprint readers, but also the ability to conduct fingerprint reader interoperability studies. Fingerprint reader interoperability refers to how robust fingerprint recognition systems are to variations in the images acquired by different types of fingerprint readers. To build universal 3D fingerprint targets, we adopt a molding and casting framework consisting of (i) digital mapping of fingerprint images to a negative mold, (ii) CAD modeling a scaffolding system to hold the negative mold, (iii) fabricating the mold and scaffolding system with a high resolution 3D printer, (iv) producing or mixing a material with similar electrical, optical, and mechanical properties to that of the human finger, and (v) fabricating a 3D fingerprint target using controlled casting. Our experiments conducted with PIV and Appendix F certified optical (contact and contactless) and capacitive fingerprint readers demonstrate the usefulness of universal 3D fingerprint targets for controlled and repeatable fingerprint reader evaluations and also fingerprint reader interoperability studies.

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confidence: 87%

Section: Introductionmentioning

confidence: 99%

Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations

Engelsma

Arora

Jain

et al. 2018

IEEE Trans.Inform.Forensic Secur.

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“…2 (a)). See also NIST fingerprint evaluation FpVTE 2012 [10]. Failures of the COTS A minutiae-based matcher (minutiae annotated with COTS A).…”

Section: Introductionmentioning

confidence: 99%

Learning a Fixed-Length Fingerprint Representation

Engelsma

Cao²,

Jain

2021

IEEE Trans. Pattern Anal. Mach. Intell.

108

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We present DeepPrint, a deep network, which learns to extract fixed-length fingerprint representations of only 200 bytes. DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, into the deep network architecture to maximize the discriminative power of its representation. The compact, DeepPrint representation has several advantages over the prevailing variable length minutiae representation which (i) requires computationally expensive graph matching techniques, (ii) is difficult to secure using strong encryption schemes (e.g. homomorphic encryption), and (iii) has low discriminative power in poor quality fingerprints where minutiae extraction is unreliable. We benchmark DeepPrint against two top performing COTS SDKs (Verifinger and Innovatrics) from the NIST and FVC evaluations. Coupled with a re-ranking scheme, the DeepPrint rank-1 search accuracy on the NIST SD4 dataset against a gallery of 1.1 million fingerprints is comparable to the top COTS matcher, but it is significantly faster (DeepPrint: 98.80% in 0.3 seconds vs. COTS A: 98.85% in 27 seconds). To the best of our knowledge, the DeepPrint representation is the most compact and discriminative fixed-length fingerprint representation reported in the academic literature.

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“…In addition, homeland security agencies deploy Automated Fingerprint Identification Systems (AFIS) for watch list comparisons of passengers arriving at ports of entry and national registry systems for deduplication before issuing an ID. While state of the art AFIS are quite accurate and robust [2], their recognition accuracy drops when they encounter noisy fingerprint images whose friction ridges are degraded or destroyed. Intentional or unintentional destruction of friction ridge patterns, degrades the information content of a fingerprint image and therefore increases the error rate of an AFIS.…”

Section: Introductionmentioning

confidence: 99%

Altered Fingerprints: Detection and Localization

Tabassi

Chugh

Deb

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS. This paper proposes a method for detection and localization of fingerprint alterations. Our main contributions are: (i) design and train CNN models on fingerprint images and minutiae-centered local patches in the image to detect and localize regions of fingerprint alterations, and (ii) train a Generative Adversarial Network (GAN) to synthesize altered fingerprints whose characteristics are similar to true altered fingerprints. A successfully trained GAN can alleviate the limited availability of altered fingerprint images for research. A database of 4, 815 altered fingerprints from 270 subjects, and an equal number of rolled fingerprint images are used to train and test our models. The proposed approach achieves a True Detection Rate (TDR) of 99.24% at a False Detection Rate (FDR) of 2%, outperforming published results. The synthetically generated altered fingerprint dataset will be open-sourced.

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Fingerprint Vendor Technology Evaluation

Cited by 38 publications

References 0 publications

Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations

Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations

Learning a Fixed-Length Fingerprint Representation

Altered Fingerprints: Detection and Localization

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