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Proceedings Paper

A review of state-of-the-art speckle reduction techniques for optical coherence tomography fingertip scans
Author(s): Luke Nicholas Darlow; Sharat Saurabh Akhoury; James Connan
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Paper Abstract

Standard surface fingerprint scanners are vulnerable to counterfeiting attacks and also failure due to skin damage and distortion. Thus a high security and damage resistant means of fingerprint acquisition is needed, providing scope for new approaches and technologies. Optical Coherence Tomography (OCT) is a high resolution imaging technology that can be used to image the human fingertip and allow for the extraction of a subsurface fingerprint. Being robust toward spoofing and damage, the subsurface fingerprint is an attractive solution. However, the nature of the OCT scanning process induces speckle: a correlative and multiplicative noise. Six speckle reducing filters for the digital enhancement of OCT fingertip scans have been evaluated. The optimized Bayesian non-local means algorithm improved the structural similarity between processed and reference images by 34%, increased the signal-to-noise ratio, and yielded the most promising visual results. An adaptive wavelet approach, originally designed for ultrasound imaging, and a speckle reducing anisotropic diffusion approach also yielded promising results. A reformulation of these in future work, with an OCT-speckle specific model, may improve their performance.

Paper Details

Date Published: 14 February 2015
PDF: 9 pages
Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944523 (14 February 2015); doi: 10.1117/12.2180537
Show Author Affiliations
Luke Nicholas Darlow, Council for Scientific and Industrial Research (South Africa)
Rhodes Univ. (South Africa)
Sharat Saurabh Akhoury, Council for Scientific and Industrial Research (South Africa)
James Connan, Rhodes Univ. (South Africa)

Published in SPIE Proceedings Vol. 9445:
Seventh International Conference on Machine Vision (ICMV 2014)
Antanas Verikas; Branislav Vuksanovic; Petia Radeva; Jianhong Zhou, Editor(s)

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