Live Acquisition of Internal Fingerprint With Automated Detection of Subsurface Layers Using OCT
- Authors
- Aum, Jaehong; Kim, Ji-Hyun; Jeong, Jichai
- Issue Date
- 15-1월-2016
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Biomedical optical imaging; biometrics; fingerprint recognition; optical coherence tomography; pattern recognition
- Citation
- IEEE PHOTONICS TECHNOLOGY LETTERS, v.28, no.2, pp.163 - 166
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE PHOTONICS TECHNOLOGY LETTERS
- Volume
- 28
- Number
- 2
- Start Page
- 163
- End Page
- 166
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/89792
- DOI
- 10.1109/LPT.2015.2487962
- ISSN
- 1041-1135
- Abstract
- Recently, optical coherence tomography (OCT) was introduced as a novel fingerprint scanning technique. This approach is resistant to fake fingerprint attacks, and is robust against poor fingertip conditions, i.e., wet or stained fingers. The method proceeds by scanning a fingertip in three dimensions and capturing a fingerprint from the subsurface layer. Although OCT has the potential to be widely used as a new standard in fingerprint scanning, it is hindered by its low scanning speed and the lack of computing power available for reproducing raw OCT data into images in real time; for instance, this process can take minutes to obtain barely one fingerprint image. In this letter, we introduce a novel spectral-domain OCT-based 3-D fingerprint scanner that is capable of obtaining an internal fingerprint image within 2 s. In order to obtain internal fingerprint images from raw OCT data in real time, we used graphics processing unit for massive parallel computation, along with an automated method for extracting the internal fingerprint from a 3-D scan of a fingertip. In addition, the robustness of the OCT fingerprint scanner was established by comparing fingerprint images-of wet, stained, and damaged fingertips-that were obtained by the OCT system with those from a commercially available optical fingerprint scanner.
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Collections - Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
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