Artificial fingerprint recognition by using optical coherence
tomography with autocorrelation analysis
Yezeng Cheng and Kirill V. Larin
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 poten-
tially used in automatic recognition systems.© 2006 Optical Society of America
100.5010, 110.4500, 160.4670, 170.6930.
Accurate automatic identification and recognition of
a person is currently considered a cornerstone of
many security applications, especially in this modern
digital age. There are several different biometric
techniques to recognize a person. Behavioral charac-
teristics, such as keystrokes dynamics and signature
dynamics, and physical characteristics, such as iris
recognition, face recognition, and fingerprint recogni-
tion, are becoming the dominant methods for biomet-
Among all biometric techniques, the fingerprint
recognition is the most popular method. This method
has the following advantages: (1) universality—the
population that has legible fingerprints exceeds the
population that possesses the passports; (2) high
distinctiveness—even identical twins who share the
same DNA have different fingerprints; and (3) high
performance—the fingerprint is one of the most ac-
curate biometric characteristics with low FAR (false
accept rate) and FRR (false reject rate). Already at
the age of seven months, a fetus’s fingerprints are
fully developed, and characteristics of the finger-
prints do not change throughout the lifetime except
for injury or skin disease. However, after a small
injury to a fingertip, the pattern will grow back as
the fingertip heals.2The uniqueness of the finger-
prints can be determined by the pattern of minutia
locations,3,4local ridge orientation data, and ridge-
orientation data combined with minutia locations.5
Therefore fingerprint recognition has become synon-
ymous with the reliable method of personal identifi-
cation. The FBI currently maintains more than 200
million fingerprint records on file. However, artificial
finger dummies with embedded fingerprints, made
using only $10 worth of household supplies, may
easily spoof common fingerprint systems.6Therefore
fingerprint recognition systems need to be improved
to protect against different fraudulent methods.
During the past several years, significant improve-
ments have been made by several scientific groups to
enhance the robustness of the fingerprint readers
based on the recognition of the surface topology. A
smart card holder authentication system, which
joined fingerprint verification with personal identifi-
random phase encoding scheme, was described in
Ref. 7. By using an optimized template for core de-
tection, the FRR was improved. Making use of a
fast fingerprint enhancement algorithm, which could
adaptively improve the clarity of ridge and valley
structures of the input fingerprint images (based on
the estimated local ridge orientation and frequency),
a goodness index, and verification accuracy could also
The authors are with the Biomedical Optics Laboratory, Bio-
medical Engineering Program, University of Houston, N207 Engi-
neering Building 1, Houston, Texas 77204-4006. K. V. Larin’s
e-mail address is firstname.lastname@example.org.
Received 11 May 2006; revised 17 August 2006; accepted 23
August 2006; posted 29 August 2006 (Doc. ID 70746).
© 2006 Optical Society of America
9238APPLIED OPTICS ? Vol. 45, No. 36 ? 20 December 2006
measurement by interferometry with partially coherent light,”
Opt. Lett. 13, 186–188 (1988).
11. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G.
Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A.
Puliafito, and J. G. Fujimoto, “Optical coherence tomography,”
Science 254, 1178–1181 (1991).
12. R. N. Bracewell, The Fourier Transform and Its Applications,
3rd ed., McGraw-Hill Series in Electrical and Computer En-
gineering. Circuits and Systems (McGraw-Hill, 2000), pp. xx,
13. J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical
coherence tomography,” J. Biomed. Opt. 4, 95–105 (1999).
14. J. Rogowska and M. E. Brezinski, “Evaluation of the adaptive
speckle suppression filter for coronary optical coherence to-
mography imaging,” IEEE Trans. Med. Imaging 19, 1261–
15. N. Iftimia, B. E. Bouma, and G. J. Tearney, “Speckle reduction
in optical coherence tomography by path length encoded an-
gular compounding,” J. Biomed. Opt. 8, 260–263 (2003).
16. A. I. Kholodnykh, I. Y. Petrova, K. V. Larin, M. Motamedi, and
R. O. Esenaliev, “Precision of measurement of tissue optical
properties with optical coherence tomography,” Appl. Opt. 42,
17. M. Pircher, E. Gotzinger, R. Leitgeb, A. F. Fercher, and C. K.
Hitzenberger, “Speckle reduction in optical coherence tomog-
raphy by frequency compounding,” J. Biomed. Opt. 8, 565–569
18. J. Kim, D. T. Miller, E. Kim, S. Oh, J. Oh, and T. E. Milner,
“Optical coherence tomography speckle reduction by a par-
tially spatially coherent source,” J. Biomed. Opt. 10, 064034
19. D. A. Zimnyakov, V. P. Ryabukho, and K. V. Larin, “Microlens
effect due to the diffraction of focused beams on large-scale
phase screens,” JETP Lett. 20, 14–19 (1994).
20. K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Bar-
ton, “Texture, analysis of optical coherence tomography im-
ages: feasibility for tissue classification,” J. Biomed. Opt. 8,
21. D. A. Zimnyakov and M. A. Vilensky, “Blink speckle spectros-
copy of scattering media,” Opt. Lett. 31, 429–431 (2006).
22. D. A. Zimnyakov, D. N. Agafonov, A. P. Sviridov, A. I.
Omel’chenko, L. V. Kuznetsova, and V. N. Bagratashvili,
“Speckle-contrast monitoring of tissue thermal modification,”
Appl. Opt. 41, 5989–5996 (2002).
23. T. van der Putte and J. Keuning, “Biometrical fingerprint rec-
ognition: don’t get your fingers burned,” presented at the
Fourth Working Conference on Smart Card Research and Ad-
vanced Applications, Bristol, UK, 20–22 September 2000.
24. R. K. Manapuram, M. Ghosn, and K. V. Larin, “Identification
of artificial fingerprints using optical coherence tomography
technique,” Asian J. Phys. 15, 15–27 (2006).
20 December 2006 ? Vol. 45, No. 36 ? APPLIED OPTICS9245