Abstract

We propose a new technique for security verification of personal documents and other forms of personal identifications such as ID cards, passports, or credit cards. In this technique a primary pattern that might be a phase-encoded image is convolved by a random code. The information is phase encoded on the personal document. Therefore the information cannot be reproduced by an intensity detector such as a CCD camera. An optical processor based on the nonlinear joint transform correlator is used to perform the verification and the validation of documents with this technique. By verification of the biometrics information and the random code simultaneously, the proposed optical system determines whether a card is authentic or is being used by an authorized person. We tested the performance of the optical system for security and validation in the presence of input noise and in the presence of distortion of the information on the card. The performance of the proposed method is evaluated by use of a number of metrics. Statistical analysis of the system is performed to investigate the noise tolerance and the discrimination against false inputs for security verification.

© 1998 Optical Society of America

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References

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    [CrossRef]
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1996

T. Grycewicz, B. Javidi, “Experimental comparison of binary joint transform correlators used for fingerprint identification,” Opt. Eng. 35, 2519–2525 (1996).
[CrossRef]

1995

J. Rodolfo, H. Rajbenbach, J.-P. Huignard, “Performance of a photorefractive joint transform correlator for fingerprint identification,” Opt. Eng. 34, 1166–1171 (1995).
[CrossRef]

Ph. Réfrégier, B. Javidi, “Optical image encryption using input and Fourier plane random phase encoding,” Opt. Lett. 20, 767–769 (1995).
[CrossRef]

1994

S. Maze, Ph. Réfrégier, “Optical correlation: influence of the coding of the input image,” Appl. Opt. 33, 6788–6796 (1994).
[CrossRef] [PubMed]

B. Javidi, J. L. Horner, “Optical pattern recognition for validation and security verification,” Opt. Eng. 33, 1752–1756 (1994).
[CrossRef]

1993

1992

1991

P. K. H. Fielding, J. L. Horner, C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng. 30, 1958–1961 (1991).
[CrossRef]

1989

1984

1968

1966

Fielding, P. K. H.

P. K. H. Fielding, J. L. Horner, C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng. 30, 1958–1961 (1991).
[CrossRef]

Goodman, J. W.

Grycewicz, T.

T. Grycewicz, B. Javidi, “Experimental comparison of binary joint transform correlators used for fingerprint identification,” Opt. Eng. 35, 2519–2525 (1996).
[CrossRef]

Horner, J. L.

B. Javidi, J. L. Horner, “Optical pattern recognition for validation and security verification,” Opt. Eng. 33, 1752–1756 (1994).
[CrossRef]

P. K. H. Fielding, J. L. Horner, C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng. 30, 1958–1961 (1991).
[CrossRef]

Huignard, J.-P.

J. Rodolfo, H. Rajbenbach, J.-P. Huignard, “Performance of a photorefractive joint transform correlator for fingerprint identification,” Opt. Eng. 34, 1166–1171 (1995).
[CrossRef]

Javidi, B.

T. Grycewicz, B. Javidi, “Experimental comparison of binary joint transform correlators used for fingerprint identification,” Opt. Eng. 35, 2519–2525 (1996).
[CrossRef]

Ph. Réfrégier, B. Javidi, “Optical image encryption using input and Fourier plane random phase encoding,” Opt. Lett. 20, 767–769 (1995).
[CrossRef]

B. Javidi, J. L. Horner, “Optical pattern recognition for validation and security verification,” Opt. Eng. 33, 1752–1756 (1994).
[CrossRef]

B. Javidi, “Nonlinear joint power spectrum-based optical correlation,” Appl. Opt. 28, 2358–2367 (1989).
[CrossRef] [PubMed]

B. Javidi, “Nonlinear joint transform correlators,” in Real-Time Optical Information Processing, B. Javidi, J. L. Horner, eds. (Academic, New York, 1994), pp. 115–183.

Kogelnik, H.

Kowalczyk, M.

Li, H.-Y.

Makekau, C. K.

P. K. H. Fielding, J. L. Horner, C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng. 30, 1958–1961 (1991).
[CrossRef]

Maze, S.

Paek, E. G.

C. L. Wilson, C. I. Watson, E. G. Paek, “Combined optical and neural network fingerprint matching,” in Optical Pattern Recognition VIII, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE3073, 373–383 (1997).
[CrossRef]

Pennington, K. S.

Psaltis, D.

Qiao, Y.

Rajbenbach, H.

J. Rodolfo, H. Rajbenbach, J.-P. Huignard, “Performance of a photorefractive joint transform correlator for fingerprint identification,” Opt. Eng. 34, 1166–1171 (1995).
[CrossRef]

Réfrégier, Ph.

Rodolfo, J.

J. Rodolfo, H. Rajbenbach, J.-P. Huignard, “Performance of a photorefractive joint transform correlator for fingerprint identification,” Opt. Eng. 34, 1166–1171 (1995).
[CrossRef]

Watson, C. I.

C. L. Wilson, C. I. Watson, E. G. Paek, “Combined optical and neural network fingerprint matching,” in Optical Pattern Recognition VIII, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE3073, 373–383 (1997).
[CrossRef]

Weaver, C. J.

Wilson, C. L.

C. L. Wilson, C. I. Watson, E. G. Paek, “Combined optical and neural network fingerprint matching,” in Optical Pattern Recognition VIII, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE3073, 373–383 (1997).
[CrossRef]

Appl. Opt.

J. Opt. Soc. Am.

J. Opt. Soc. Am. A

Opt. Eng.

T. Grycewicz, B. Javidi, “Experimental comparison of binary joint transform correlators used for fingerprint identification,” Opt. Eng. 35, 2519–2525 (1996).
[CrossRef]

P. K. H. Fielding, J. L. Horner, C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng. 30, 1958–1961 (1991).
[CrossRef]

J. Rodolfo, H. Rajbenbach, J.-P. Huignard, “Performance of a photorefractive joint transform correlator for fingerprint identification,” Opt. Eng. 34, 1166–1171 (1995).
[CrossRef]

B. Javidi, J. L. Horner, “Optical pattern recognition for validation and security verification,” Opt. Eng. 33, 1752–1756 (1994).
[CrossRef]

Opt. Lett.

Other

B. Javidi, “Nonlinear joint transform correlators,” in Real-Time Optical Information Processing, B. Javidi, J. L. Horner, eds. (Academic, New York, 1994), pp. 115–183.

C. L. Wilson, C. I. Watson, E. G. Paek, “Combined optical and neural network fingerprint matching,” in Optical Pattern Recognition VIII, D. P. Casasent, T.-H. Chao, eds., Proc. SPIE3073, 373–383 (1997).
[CrossRef]

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Figures (11)

Fig. 1
Fig. 1

Optical setup based on the nonlinear JTC for security verification of an object. L, lens; BS, beam splitter; IFFT, inverse fast Fourier transform.

Fig. 2
Fig. 2

Fingerprints used in the tests: (a) The authorized fingerprint. (b) The unauthorized fingerprint.

Fig. 3
Fig. 3

Nonlinear joint transform correlation results for the verification and the validation of inputs with a nonlinearity index of k = 0.3: (a) output-correlation intensity for the authentic input, (b) output for the authorized input with an unauthorized code, (c) output for an unauthorized input with the authorized code, (d) output for an unauthorized input and an unauthorized code.

Fig. 4
Fig. 4

Input primary image [fingerprint of Fig. 2(a)] corrupted by additive noise: (a) zero-mean white noise with a standard deviation of 0.3 and (b) zero-mean color noise with a standard deviation of 0.3 and a bandwidth of 15.

Fig. 5
Fig. 5

Simulation results for the proposed system in the presence of additive white noise versus the nonlinearity index k. The results correspond to white noise on the reference image with a standard deviation of 0.7 and different input additive-noise levels: *, σ = 0.0; ×, σ = 0.3; ○, σ = 0.5; +, σ = 0.7. The curves marked with an asterisk correspond to the performance of the system in the absence of input noise. (a) Average of the DR between the authorized card and the unauthorized card with an unauthorized fingerprint. (b) Average of the DR between the authorized card and the unauthorized card with an unauthorized code. (c) Output SNR. (d) Output POE.

Fig. 6
Fig. 6

Simulation results of the proposed system in the presence of additive color noise with a bandwidth of 15 versus the index nonlinearity k. The results correspond to additive color noise on the reference with a standard deviation of 0.7 and different input additive color noise levels: *, σ = 0.0; ×, σ = 0.3; ○, σ = 0.5; +, σ = 0.7. The curves marked with an asterisk correspond to the performance of the system in the absence of input noise. (a) Average of the DR between the authorized card and the unauthorized card with an unauthorized fingerprint. (b) Average of the DR between the authorized card and the unauthorized card with an unauthorized code. (c) Output SNR. (d) Output POE.

Fig. 7
Fig. 7

Input primary pattern with missing data when 25% of the authorized fingerprint is blocked.

Fig. 8
Fig. 8

Correlation results for the verification and the validation of cards with a nonlinearity index of k = 0.3. The authorized fingerprint has 25% missing data. (a) Output-correlation intensity for the authentic card. (b) Output-correlation intensity for authorized input with an unauthorized code. (c) Output-correlation intensity for unauthorized input with an authorized code. (d) Output-correlation intensity for unauthorized input with an unauthorized code.

Fig. 9
Fig. 9

Correlation results for discrimination between authorized and unauthorized inputs by use of the rotation-invariant reference image encoded on the card. (a) Results for authorized input. (b) Results for unauthorized input with an unauthorized fingerprint. (c) Results for unauthorized input with an unauthorized code. (d) Results for unauthorized input with an unauthorized fingerprint and unauthorized code.

Fig. 10
Fig. 10

Binary phase information encoded at input.

Fig. 11
Fig. 11

Correlation results for discrimination between the authorized and the unauthorized cards by use of the binarization of the reference encoded at the input. (a) Results for authorized input. (b) Results for unauthorized input with an unauthorized fingerprint. (c) Results for unauthorized input with an unauthorized code. (d) Results for unauthorized input with an unauthorized fingerprint and unauthorized code.

Equations (10)

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r x ,   y = exp i π f x ,   y / Max f x ,   y c x ,   y ,
r ¯ x ,   y = r x ,   y | r x ,   y | .
r B ¯ x ,   y = - 1 1 ,     if   Re r x ,   y < 0 if   Re r x ,   y 0 ,
Output x ,   y = s x ,   y * k r ¯ x ,   y ,
Output x ,   y = s x ,   y * k r ¯ B x ,   y .
DR = | max AC x ,   y | 2 | max CC x ,   y | 2 ,
AC x ,   y = ( exp i π f x ,   y / Max f x ,   y c x ,   y ) * k × exp i π f x ,   y / Max f x ,   y c x ,   y | exp i π f x ,   y / Max f x ,   y c x ,   y | ,
CC x ,   y = ( exp { i π g x ,   y / Max g x ,   y ] a x ,   y ) * k × exp i π f x ,   y / Max f x ,   y c x ,   y | exp { i π f x ,   y / Max f x ,   y } c x ,   y | ,
Output x ,   y = exp i π f x ,   y + n p x ,   y / Max f x ,   y + n p x ,   y c x ,   y * k × exp i π f x ,   y / Max f x ,   y c x ,   y | exp i π f x ,   y / Max f x ,   y c x ,   y | + n c x ,   y ,
p ¯ x ,   y = α exp i π f α x ,   y / Max f α x ,   y c x ,   y α exp i π f α x ,   y / Max ( f α x ,   y c ( x ,   y ) ,

Metrics