Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81954
Title: Contrast Enhanced Subsurface Fingerprint Detection Using High-Speed Optical Coherence Tomography
Authors: Yu, Xiaojun
Xiong, Qiaozhou
Luo, Yuemei
Wang, Nanshuo
Wang, Lulu
Tey, Hong Liang
Liu, Linbo
Keywords: Fingerprint identification
Biometrics
Issue Date: 2016
Source: Yu, X., Xiong, Q., Luo, Y., Wang, N., Wang, L., Tey, H. L., et al. (2016). Contrast Enhanced Subsurface Fingerprint Detection Using High-Speed Optical Coherence Tomography. IEEE Photonics Technology Letters, 29(1), 70-73.
Series/Report no.: IEEE Photonics Technology Letters
Abstract: Spectral-domain optical coherence tomography (SD-OCT) has been demonstrated to be a viable tool in forensic science for fingerprint detection, yet it still suffers from certain practical issues, e.g., the limited scanning speed and low image contrast. In this letter, we report a high-speed SD-OCT together with an image contrast enhancement mechanism for reliable subsurface fingerprint detection. The constructed SD-OCT system achieves a scanning rate up to 60k A-lines/s, and thus, both 3D volumetric images, reaching up to 20 mm × 20 mm × 1.2 mm, and en face internal fingerprint furrow pattern images could be obtained. Based upon the analyses of the papillary layer anatomical structures, the contrast enhancement technique not only suppresses image artifacts, but also is effective in detecting fingerprint spoofing. Experiments on healthy subjects have also been conducted to verify the system imaging capability and the effectiveness of the contrast-enhancement technique.
URI: https://hdl.handle.net/10356/81954
http://hdl.handle.net/10220/42284
ISSN: 1041-1135
DOI: 10.1109/LPT.2016.2628840
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [https://doi.org/10.1109/LPT.2016.2628840].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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