Article

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis

University of Houston, Houston, Texas, United States
Applied Optics (Impact Factor: 1.78). 01/2007; 45(36):9238-45. DOI: 10.1364/AO.45.009238
Source: PubMed

ABSTRACT Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

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    • "OCT is able to provide a high resolution three-dimensional representation of fingertip skin. This technique has been shown as a potential means of mitigating disadvantages associated with conventional fingerprints [5], [6], [7], [8], [9]. "
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    ABSTRACT: Current surface fingerprint scanners measure the surface topography of skin, resulting in vulnerabilities to surface skin erosion, distortion due to contact with the scanner, and fingerprint counterfeiting. An improved means of fingerprint acquisition is necessitated in these facts. By employing an imaging technique known as Optical Coherence Tomography to the human fingertip skin, a three-dimensional digital reconstruction of subsurface layers of skin can be used for the extraction of an internal fingerprint. The internal fingerprint is robust towards counterfeiting, damage, and distortion, thus providing a replacement for the surface fingerprint. However, OCT scans are corrupted by speckle noise and have low contrast, resulting in a poor quality fingerprint representation. This research applies image enhancement procedures to OCT scan images to improve internal fingerprint quality. Furthermore, a novel internal fingerprint mapping technique is presented: papillary junction detection followed by defined region mapping. With a RMS-contrast improvement of 97%, this technique yields a much higher quality internal fingerprint when compared to previous techniques.
    Digital Information, Networking, and Wireless Communications (DINWC), 2015 Third International Conference on, Moscow, Russia; 02/2015
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    • "OCT is able to provide a high resolution three-dimensional representation of fingertip skin. This technique has been shown as a potential means of mitigating disadvantages associated with conventional fingerprints [5], [6], [7], [8], [9]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Current surface fingerprint scanners measure the surface topography of skin, resulting in vulnerabilities to surface skin erosion, distortion due to contact with the scanner, and fingerprint counterfeiting. An improved means of fingerprint acquisition is necessitated in these facts. By employing an imaging technique known as Optical Coherence Tomography to the human fingertip skin, a three-dimensional digital reconstruction of subsurface layers of skin can be used for the extraction of an internal fingerprint. The internal fingerprint is robust towards counterfeiting, damage, and distortion, thus providing a replacement for the surface fingerprint. However, OCT scans are corrupted by speckle noise and have low contrast, resulting in a poor quality fingerprint representation. This research applies image enhancement procedures to OCT scan images to improve internal fingerprint quality. Furthermore, a novel internal fingerprint mapping technique is presented: papillary junction detection followed by defined region mapping. With a RMS-contrast improvement of 97%, this technique yields a much higher quality internal fingerprint when compared to previous techniques.
    Third International Conference on Digital Information, Networking, and Wireless Communications (DINWC), Moscow, Russia; 02/2015
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    • "In this work, the focus is constrained to the segmentation of the internal fingerprint, the papillary layer, from swept source optical coherence tomography (SS-OCT) 3- D images. Time domain OCT has been used to image subsurface finger tissue for biometrics [4],[5]; while in the Fourier domain OCT, swept source OCT is preferred over spectral domain OCT as it minimizes dispersion and speckle effects better [6],[7]. Spectral domain OCT was used to extract intricate biometric details such as the distribution of sweat pores and the pattern of capillary bed [8], while [9] used correlation mapping OCT. "
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    ABSTRACT: Biometric fingerprint scanners scan the external skin features onto a 2-D image. The performance of the automatic fingerprint identification system suffers if the finger skin is wet, worn out, fake fingerprint is used et cetera. In this paper, we present an automatic segmentation of the papillary layer method, in 3-D swept source optical coherence tomography (SS-OCT) images. The papillary contour represents the internal fingerprint, which does not suffer external skin problems. The slices composing the 3-D image are filtered by the regularized Perona and Malik partial differential equations filter to minimize the effect of speckle noise. Then the corneum stratum is detected; which in turn leads to the extraction of the epidermis using prior knowledge of the epidermis depth. The epidermis is used as the target of the novelty detection that is applied to the image slices. The contour of the papillary layer is segmented as the boundary between the target and rejection classes resulting from novelty detection. The papillary contours are consistent with those segmented manually; with the modified Williams index above 0.9400 on average. The 3-D papillary contour represents an internal fingerprint.
    The Second International Symposium on Computing and Networking, Shizuoka; 10/2014
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