Performance analysis of a Hybrid fingerprint extracted from optical coherence tomography fingertip scans

Darlow, Luke Nicholas; Connan, James; Singh, Ann

doi:10.1109/icb.2016.7550045

Cited by 13 publications

(11 citation statements)

References 25 publications

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“…Direct comparison with the method of Darlow et al [3] is difficult, since we were not able to obtain the codes/executables from the authors and we do not have any opportunity to share the data either due to their sensitive nature. However, we believe our method clearly outperforms the method of Darlow et al [3] for the following reasons:…”

Section: Resultsmentioning

confidence: 99%

“…In addition, majority of the studies assume a finger pressed against a flat surface (e.g. glass) during the scanning, which greatly simplifies the processing challenges (since the fingerprint surface is flattened) but introduces a number of disadvantages shared with standard 2D fingerprint sensors, such as difficulty to scan wet, greasy, dry or soft-skinned fingers, which could otherwise be easily overcome by the OCT [3,8].…”

Section: Related Workmentioning

confidence: 99%

“…Regarding the work that satisfies the challenging constraints mentioned above, we are aware only of the promising works by Darlow et al [3][4][5].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Fingerprints Captured Using Optical Coherence Tomography

Sousedik

Breithaupt

Bours

2017

Lecture Notes in Computer Science

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We propose a technique for analysis of fingerprints scanned free-air (not pressed against a glass) with Optical Coherence Tomography (OCT). Fingerprints from the surface and subdermal parts of the finger are extracted from a 2GB volumetric scan in cca. 2 s using our specialized technique and GPU acceleration on GeForce GTX 980. The technique provides fingerprints that perform with promising error rates that demonstrate the potential of the OCT for improved fingerprint identification, as well as its potential for prevention of biometric spoofing (PAD).

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Classification of Fingerprints Captured Using Optical Coherence Tomography

Sousedik

Breithaupt

Bours

2017

Lecture Notes in Computer Science

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show abstract

“…We are also currently experimenting with a solution for segmentation of the surface & internal fingerprint at acceptable processing times -a problem that has been recently addressed in a promising fashion by Darlow et al [12].…”

Section: Discussionmentioning

confidence: 99%

“…Promising recent studies by Darlow et al [12][13] [14] focus on the fast extraction of the 2D internal and surface fingerprints from 3D volumetric fingerprint scans. Their studies are carried out using a commercially available FD-OCT setup capable of scanning in a 15x15mm fingerprint area.…”

Section: A Oct Fingerprintingmentioning

confidence: 99%

Full-fingerprint volumetric subsurface imaging using Fourier-domain Optical Coherence Tomography

Sousedik

Breithaupt

2017

2017 5th International Workshop on Biometrics and Forensics (IWBF)

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Abstract-Despite the long history of automated fingerprint recognition, the state-of-the-art 2D sensors still struggle with abraded, dry, wet or greasy fingers, as well as extra-soft skin of the infant fingerprints. But above all, the current sensors suffer from vulnerabilities to presentation attacks, which greatly limits their applicability in unsupervised scenarios such as border control. Optical Coherence Tomography (OCT) represents an alternative approach with a high potential to address the existing challenges. In this paper, we present a novel fingerprint sensor based on Fourier Domain Optical Coherence Tomography (FD-OCT), capable of capturing a 3D volumetric full fingerprint scan from a 2x2cm area, along with subsurface details such as sweat glands and the internal fingerprint. The novelty of our setup lies in the ability of FD-OCT-scanning a full 3D volumetric fingerprint at high-resolution at speeds that demonstrate the usability of 3D OCT in real-world applications.

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Introduction to Trends in Fingerprint Identification

Kumar

2018

Contactless 3D Fingerprint Identification

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Performance analysis of a Hybrid fingerprint extracted from optical coherence tomography fingertip scans

Cited by 13 publications

References 25 publications

Classification of Fingerprints Captured Using Optical Coherence Tomography

Classification of Fingerprints Captured Using Optical Coherence Tomography

Full-fingerprint volumetric subsurface imaging using Fourier-domain Optical Coherence Tomography

Introduction to Trends in Fingerprint Identification

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