Enhanced spoof-proofing of fingerprint readers by optical coherence tomography
Kirill V. Larin and Yezeng Cheng
Nondestructive depth-resolved imaging of molecular diffusion in tissues can assist in development of novel therapeutic agents and drug delivery systems as well as provide a novel tool for early diagnosis of various epithelial disorders.
Artificial fingerprint dummies can easily spoof current commercial fingerprint readers, while images from optical coherence tomography (OCT) technique expose the fraud dummies at all times. An autocorrelation analysis of speckle-noise of the OCT images can be potentially used in automatic recognition systems. In the modern digital era, accurate automatic identification of a person is considered a cornerstone of many security applications. Among all biometric techniques, fingerprint recognition is widely accepted as the most common method, because of its universality, high distinctiveness and high performance. However, artificial finger dummies with embedded fingerprints, made using only $10 worth of household supplies, may easily spoof common fingerprint systems.1 Therefore fingerprint recognition systems need to be improved to protect against different fraudulent methods. During the past several years, significant improvements have been made by several scientific groups to enhance the robustness of the fingerprint readers based on the recognition of the surface topology. For instance, a smart card holder authentication system, which joined fingerprint verification with personal identification number (PIN) verification by applying a double random phase encoding scheme, was described in Ref.2 However, these improvements in the fingerprint recognition methods concentrated on decreasing FAR (false accept rate) and FRR (false reject rate) or shortening the scanning time. Hence, they do not prevent system bypass with artificial fingerprints. Recently, we demonstrated that optical coherence tomography (OCT) technique,3 which provided in-depth imaging with high resolution (Fig. 1), could successfully identify artificial materials commonly used for spoofing optical fingerprint scanning systems. 4
Figure 1. Schematic of the OCT system used in these studies. PD, photodetector; ADC, analog-to-digital converter.
To make artificial fingerprint dummies (Figure 2), we used general household materials that could be found in any supermarket and grocery store. A plasticene (Dixon Ticonderoga Company, Mexico), household cement (ITW Devcon Corp., Mass.), and a liquid silicon rubber (GE Silicones, General Electric Co., New York) were used.
Figure 2. Gross pictures of (a) plasticene female mold and (b) artificial fingerprint dummy (male mold).
Figure 3(a) shows the typical OCT image of a finger skin below a dummy layer with artificial fingerprints. Three layers of human skin (stratum corneum, epidermis, and dermis) are clearly visible. The corresponding 1D OCT signal is shown in Figure 3(b). The dummy layer is a homogeneous media, as illustrated by the OCT signal curve, and has significantly lower scattering profile than that of the skin.
Figure 3. OCT images (a) obtained from the artificial fingerprint dummy used to bypass fingerprint reader device placed over a real finger and (b) corresponding OCT signal curve.
The artificial fingerprint dummies were tested with the Microsoft fingerprint reader device (Microsoft Fingerprint Reader, model 1033, Redmond, Washington) and OCT system. When these artificial dummies were applied to the reader, the FARs were from 80% to 100% (each dummy was tried at least ten times). However, the artificial fingerprint dummies were always detected by the OCT system both visually (in 2D images and in corresponding OCT signal curves) and after processing with the autocorrelation analysis (Figure 4). Autocorrelation analysis is a commonly used method in signal processing to analyze functions and series. Here, we applied it to test whether the OCT images recorded from artificial materials and real skin can be distinguished depending on their speckle-noise pattern.
Figure 4. Autocorrelation curves were generated from the OCT image at the regions of (a) the artificial material and (b) human skin, respectively.
Conclusion
We demonstrated that the high-resolution OCT technique could be successfully applied for the identification of artificial materials commonly used to make fake fingerprints. Autocorrelation analysis could potentially be used in automatic fingerprint recognition systems.
Acknowledgement
This work was supported by a grant from the University of Houston (ST 35906). We thank Mohamad M. Ghosn, Ravi Kiran Manapuram, and Steven Ivers for their help with experiments, discussion of the data, and photographs of fingerprint molds and dummies.
References:
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