Abstract

A parallel volume holographic optical fingerprint recognition system robust to fingerprint translation, rotation and nonlinear distortion is proposed. The optical fingerprint recognition measures the similarity by using the optical filters of multiplexed holograms recorded in the holographic media. A fingerprint is encoded into multiple template data pages based on the local minutiae structure coding method after it is adapted for the optical data channel. An improved filter recording time schedule and a post-filtering calibration technology are combined to suppress the calculating error from the large variations in data page filling ratio. Experimental results tested on FVC2002 DB1 and a forensic database comprising 270,216 fingerprints demonstrate the robustness and feasibility of the system.

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References

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  1. D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition (Springer, 2009), Chap. 4, pp. 167–233.
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    [CrossRef]
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    [CrossRef]
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  12. N. K. Ratha, R. M. Bolle, V. D. Pandit, and V. Vaish, “Robust fingerprint authentication using local structural similarity,” in Proceedings of the 5th IEEE Workshop on Applications of Computer Vision (Institute of Electrical and Electronics Engineers, New York, 2000), pp. 29–34.
    [CrossRef]
  13. J. Feng, “Combining minutiae descriptors for fingerprint matching,” Pattern Recognit.41(1), 342–352 (2008).
    [CrossRef]
  14. A. A. Paulino, J. Feng, and A. K. Jain, “Latent fingerprint matching using descriptor-based hough transform,” IEEE Trans. Inf. Foren. Sec.8(1), 31–45 (2013).
    [CrossRef]
  15. J. Dai, J. Feng, and J. Zhou, “Robust and efficient ridge-based palmprint matching,” IEEE Trans. Pattern Anal. Mach. Intell.34(8), 1618–1632 (2012).
    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
  20. A. Adibi, K. Buse, and D. Psaltis, “Multiplexing holograms in LiNbO3:Fe:Mn crystals,” Opt. Lett.24(10), 652–654 (1999).
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  21. K. Curtis, K. Anderson, and M. R. Ayres, “M/# requirements for holographic data storage,” in Proceedings of the Optical Data Storage Topical Meeting, IEEE, 9–11 (2006).
    [CrossRef]
  22. L. Cao, Q. He, C. Ouyang, Y. Liao, and G. Jin, “Improvement to human-face recognition in a volume holographic correlator by use of speckle modulation,” Appl. Opt.44(4), 538–545 (2005).
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  23. http://www.neurotechnology.com/vf_sdk.html
  24. X. An, D. Psaltis, and G. W. Burr, “Thermal fixing of 10,000 holograms in LiNbO3:Fe,” Appl. Opt.38(2), 386–393 (1999).
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  25. FVC2002, http://bias.csr.unibo.it/fvc2002/default.asp

2013 (1)

A. A. Paulino, J. Feng, and A. K. Jain, “Latent fingerprint matching using descriptor-based hough transform,” IEEE Trans. Inf. Foren. Sec.8(1), 31–45 (2013).
[CrossRef]

2012 (1)

J. Dai, J. Feng, and J. Zhou, “Robust and efficient ridge-based palmprint matching,” IEEE Trans. Pattern Anal. Mach. Intell.34(8), 1618–1632 (2012).
[CrossRef] [PubMed]

2010 (1)

R. Cappelli, M. Ferrara, and D. Maltoni, “Minutia Cylinder-Code: a new representation and matching technique for fingerprint recognition,” IEEE Trans. Pattern Anal. Mach. Intell.32(12), 2128–2141 (2010).
[CrossRef] [PubMed]

2009 (1)

E. Watanabe, A. Naito, and K. Kodate, “Ultrahigh-speed compact optical correlation system using holographic disc,” Proc. SPIE7442, 74420X (2009).

2008 (1)

J. Feng, “Combining minutiae descriptors for fingerprint matching,” Pattern Recognit.41(1), 342–352 (2008).
[CrossRef]

2007 (1)

2005 (2)

2004 (1)

1999 (5)

1998 (1)

Y. Yan, G. Huang, W. Feng, G. Jin, and M. Wu, “Multichannel wavelet correlators for fingerprint identification by the use of associative storage in a photorefractive material,” Proc. SPIE3458, 259–266 (1998).
[CrossRef]

1991 (2)

K. H. Fielding, J. L. Horner, and C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng.30(12), 1958–1961 (1991).
[CrossRef]

F. H. Mok, M. C. Tackitt, and H. M. Stoll, “Storage of 500 high-resolution holograms in a LiNbO3 crystal,” Opt. Lett.16(8), 605–607 (1991).
[CrossRef] [PubMed]

Adibi, A.

Alam, M. S.

An, X.

Bal, A.

Burr, G. W.

Buse, K.

Cao, L.

Cappelli, R.

R. Cappelli, M. Ferrara, and D. Maltoni, “Minutia Cylinder-Code: a new representation and matching technique for fingerprint recognition,” IEEE Trans. Pattern Anal. Mach. Intell.32(12), 2128–2141 (2010).
[CrossRef] [PubMed]

Coufal, H.

Dai, J.

J. Dai, J. Feng, and J. Zhou, “Robust and efficient ridge-based palmprint matching,” IEEE Trans. Pattern Anal. Mach. Intell.34(8), 1618–1632 (2012).
[CrossRef] [PubMed]

El-Saba, A. M.

Feng, J.

A. A. Paulino, J. Feng, and A. K. Jain, “Latent fingerprint matching using descriptor-based hough transform,” IEEE Trans. Inf. Foren. Sec.8(1), 31–45 (2013).
[CrossRef]

J. Dai, J. Feng, and J. Zhou, “Robust and efficient ridge-based palmprint matching,” IEEE Trans. Pattern Anal. Mach. Intell.34(8), 1618–1632 (2012).
[CrossRef] [PubMed]

J. Feng, “Combining minutiae descriptors for fingerprint matching,” Pattern Recognit.41(1), 342–352 (2008).
[CrossRef]

Feng, W.

Y. Yan, G. Huang, W. Feng, G. Jin, and M. Wu, “Multichannel wavelet correlators for fingerprint identification by the use of associative storage in a photorefractive material,” Proc. SPIE3458, 259–266 (1998).
[CrossRef]

Ferrara, M.

R. Cappelli, M. Ferrara, and D. Maltoni, “Minutia Cylinder-Code: a new representation and matching technique for fingerprint recognition,” IEEE Trans. Pattern Anal. Mach. Intell.32(12), 2128–2141 (2010).
[CrossRef] [PubMed]

Fielding, K. H.

K. H. Fielding, J. L. Horner, and C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng.30(12), 1958–1961 (1991).
[CrossRef]

Grycewicz, T. J.

T. J. Grycewicz, “Techniques to improve binary joint transform correlator performance for fingerprint recognition,” Opt. Eng.38(1), 114–119 (1999).
[CrossRef]

Hanssen, H.

He, Q.

Horner, J. L.

K. H. Fielding, J. L. Horner, and C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng.30(12), 1958–1961 (1991).
[CrossRef]

Huang, G.

Y. Yan, G. Huang, W. Feng, G. Jin, and M. Wu, “Multichannel wavelet correlators for fingerprint identification by the use of associative storage in a photorefractive material,” Proc. SPIE3458, 259–266 (1998).
[CrossRef]

Jain, A. K.

A. A. Paulino, J. Feng, and A. K. Jain, “Latent fingerprint matching using descriptor-based hough transform,” IEEE Trans. Inf. Foren. Sec.8(1), 31–45 (2013).
[CrossRef]

Jin, G.

Kim, E. S.

S. H. Lee, S. Y. Yi, and E. S. Kim, “Fingerprint identification by use of a volume holographic optical correlator,” Proc. SPIE3715, 321–325 (1999).
[CrossRef]

Kobras, S.

Kodate, K.

E. Watanabe, A. Naito, and K. Kodate, “Ultrahigh-speed compact optical correlation system using holographic disc,” Proc. SPIE7442, 74420X (2009).

Lee, S. H.

S. H. Lee, S. Y. Yi, and E. S. Kim, “Fingerprint identification by use of a volume holographic optical correlator,” Proc. SPIE3715, 321–325 (1999).
[CrossRef]

Liao, Y.

Mahalanobis, A.

Makekau, C. K.

K. H. Fielding, J. L. Horner, and C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng.30(12), 1958–1961 (1991).
[CrossRef]

Maltoni, D.

R. Cappelli, M. Ferrara, and D. Maltoni, “Minutia Cylinder-Code: a new representation and matching technique for fingerprint recognition,” IEEE Trans. Pattern Anal. Mach. Intell.32(12), 2128–2141 (2010).
[CrossRef] [PubMed]

Mok, F. H.

Naito, A.

E. Watanabe, A. Naito, and K. Kodate, “Ultrahigh-speed compact optical correlation system using holographic disc,” Proc. SPIE7442, 74420X (2009).

Ni, K.

Ouyang, C.

Paulino, A. A.

A. A. Paulino, J. Feng, and A. K. Jain, “Latent fingerprint matching using descriptor-based hough transform,” IEEE Trans. Inf. Foren. Sec.8(1), 31–45 (2013).
[CrossRef]

Psaltis, D.

Qu, Z.

Savvides, M.

Stoll, H. M.

Su, P.

Tackitt, M. C.

Thornton, J.

Venkataramani, K.

Vijaya Kumar, B. V. K.

Watanabe, E.

E. Watanabe, A. Naito, and K. Kodate, “Ultrahigh-speed compact optical correlation system using holographic disc,” Proc. SPIE7442, 74420X (2009).

Wu, M.

Y. Yan, G. Huang, W. Feng, G. Jin, and M. Wu, “Multichannel wavelet correlators for fingerprint identification by the use of associative storage in a photorefractive material,” Proc. SPIE3458, 259–266 (1998).
[CrossRef]

Xie, C.

Yan, Y.

Y. Yan, G. Huang, W. Feng, G. Jin, and M. Wu, “Multichannel wavelet correlators for fingerprint identification by the use of associative storage in a photorefractive material,” Proc. SPIE3458, 259–266 (1998).
[CrossRef]

Yi, S. Y.

S. H. Lee, S. Y. Yi, and E. S. Kim, “Fingerprint identification by use of a volume holographic optical correlator,” Proc. SPIE3715, 321–325 (1999).
[CrossRef]

Zhou, J.

J. Dai, J. Feng, and J. Zhou, “Robust and efficient ridge-based palmprint matching,” IEEE Trans. Pattern Anal. Mach. Intell.34(8), 1618–1632 (2012).
[CrossRef] [PubMed]

Appl. Opt. (5)

IEEE Trans. Inf. Foren. Sec. (1)

A. A. Paulino, J. Feng, and A. K. Jain, “Latent fingerprint matching using descriptor-based hough transform,” IEEE Trans. Inf. Foren. Sec.8(1), 31–45 (2013).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

J. Dai, J. Feng, and J. Zhou, “Robust and efficient ridge-based palmprint matching,” IEEE Trans. Pattern Anal. Mach. Intell.34(8), 1618–1632 (2012).
[CrossRef] [PubMed]

R. Cappelli, M. Ferrara, and D. Maltoni, “Minutia Cylinder-Code: a new representation and matching technique for fingerprint recognition,” IEEE Trans. Pattern Anal. Mach. Intell.32(12), 2128–2141 (2010).
[CrossRef] [PubMed]

Opt. Eng. (2)

K. H. Fielding, J. L. Horner, and C. K. Makekau, “Optical fingerprint identification by binary joint transform correlation,” Opt. Eng.30(12), 1958–1961 (1991).
[CrossRef]

T. J. Grycewicz, “Techniques to improve binary joint transform correlator performance for fingerprint recognition,” Opt. Eng.38(1), 114–119 (1999).
[CrossRef]

Opt. Lett. (3)

Pattern Recognit. (1)

J. Feng, “Combining minutiae descriptors for fingerprint matching,” Pattern Recognit.41(1), 342–352 (2008).
[CrossRef]

Proc. SPIE (3)

Y. Yan, G. Huang, W. Feng, G. Jin, and M. Wu, “Multichannel wavelet correlators for fingerprint identification by the use of associative storage in a photorefractive material,” Proc. SPIE3458, 259–266 (1998).
[CrossRef]

S. H. Lee, S. Y. Yi, and E. S. Kim, “Fingerprint identification by use of a volume holographic optical correlator,” Proc. SPIE3715, 321–325 (1999).
[CrossRef]

E. Watanabe, A. Naito, and K. Kodate, “Ultrahigh-speed compact optical correlation system using holographic disc,” Proc. SPIE7442, 74420X (2009).

Other (8)

X. Jiang and W. Y. Yau, “Fingerprint minutiae matching based on the local and global structures,” in Proceedings of the 15th International Conference on Pattern Recognition2 (Institute of Electrical and Electronics Engineers, New York, 2000), pp. 1038–1041.

N. K. Ratha, R. M. Bolle, V. D. Pandit, and V. Vaish, “Robust fingerprint authentication using local structural similarity,” in Proceedings of the 5th IEEE Workshop on Applications of Computer Vision (Institute of Electrical and Electronics Engineers, New York, 2000), pp. 29–34.
[CrossRef]

K. Curtis, K. Anderson, and M. R. Ayres, “M/# requirements for holographic data storage,” in Proceedings of the Optical Data Storage Topical Meeting, IEEE, 9–11 (2006).
[CrossRef]

http://www.neurotechnology.com/vf_sdk.html

FVC2002, http://bias.csr.unibo.it/fvc2002/default.asp

D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition (Springer, 2009), Chap. 4, pp. 167–233.

P. A. Mitkas and G. W. Burr, “Volume holographic optical correlators,” in Holographic Data Storage, H. J. Coufal, D. Psaltis, and G. T. Sicebox, eds. (Springer-Verlag, 2000), pp. 429–446.

B. V. K. Vijaya Kumar, A. Mahalanobis, and R. D. Juday, Correlation Pattern Recognition (Cambridge University, 2005), Chap. 8, pp. 295–356.

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Figures (12)

Fig. 1
Fig. 1

Demonstration of the recording and retrieval processes in an angular multiplexing volume holographic correlator. SLM: spatial light modulator; FT Lens: Fourier Transform Lens.

Fig. 2
Fig. 2

A graphic representation of the local minutiae structure associated to a given minutia M: (a) the skeletonized fingerprint with its minutiae labeled; (b) the discretized cylinder of minutia M. The cylinder is rotated horizontally so that axis i is aligned to the corresponding minutia direction.

Fig. 3
Fig. 3

Demonstration of MCC representation robust to fingerprint translation, rotation and elastic deformation. (a) and (b) are two impressions from the same finger. (c)-(f) are the gray-scale and binarized MCC templates for the corresponding minutia M labeled in the center of the circle in (a) and (b), respectively.

Fig. 4
Fig. 4

The filling ratio histogram of all the tested minutiae templates with binarization threshold Thr = 0.1.

Fig. 5
Fig. 5

Typical coded minutia data pages adapting to the SLM, with a size of 640 × 480 pixels.

Fig. 6
Fig. 6

Illustration of the similarities between one minutia in a fingerprint and all the 37 minutiae obtained both theoretically and experimentally.

Fig. 7
Fig. 7

Experimental setup of the volume holographic optical correlator system. PBS, polarizing beam splitter; SLM, spatial light modulator; ST1 and ST2, shutters; L1~L3, lens; R1 and R2, reflectors; λ/2, half-wave plate.

Fig. 8
Fig. 8

Flow chart of the VHC fingerprint recognition system.

Fig. 9
Fig. 9

Eight different impressions of the same sample finger (#1) in FVC2002 DB1, remarkable translation, rotation and nonlinear distortion exist.

Fig. 10
Fig. 10

Minutiae matching results of two impressions of the same finger. (a) Optical correlation results between minutia data pages of fingerprint #1_1 (37 minutiae) and #1_3 (26 minutiae); (b) corresponding minutiae pairs linked according to optical correlation results.

Fig. 11
Fig. 11

Part of the matching results of the fingerprints with no deformation, small deformation and large deformation when searched over the database stored in the optical system.

Fig. 12
Fig. 12

(a) Matching scores of the 120,091th fingerprint with all the 270,216 fingerprints in the database. (b) Comparison of digitally and optically calculated matching scores between the sample fingerprint and its corresponding most similar 20 fingerprints.

Equations (15)

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I(f, f i )= [ g(f, f i ) ] 2 [ d x 0 d y 0 f( x 0 , y 0 ) f i * ( x 0 , y 0 ) ] 2
ψ(i,j,k)=ψ(v)=Z(v,μ,τ)= 1 1+exp[ τ(vμ) ] ,
v(i,j,k)= m t N p i,j ( C M S ( m t , p i,j ) · C M D ( m t , φ k ) ) .
C M S ( m t , p i,j )= exp( d S 2 /2 σ S 2 ) σ S 2π ,
C M D ( m t , φ k )= G D ( dϕ( φ k ,dϕ( θ M , θ m t ) ) ),
γ( v 1 , v 2 )=1 v 1 v 2 p v 1 p + v 2 p ,
γ( v 1 , v 2 )= 2× i=1 N [ v 1 (i) · v 2 (i) ] N×[ fill( v 1 )+fill( v 2 ) ] ,
γ(T, T S )= 2 i=1 Row j=1 Col [ T(i,j) · T S (i,j) ] (Row · Col) · [ fill(T)+fill( T S ) ] = 2 · T, T S (Row · Col) · [ fill(T)+fill( T S ) ] ,
t Mk = τ r ln[ (k+1) T M k k T M (k1) ], (k=0,1,2,,M1)
T 0 ' (k)={ t k · Ave_fill fill(k) if fill(k)Ave_fill max{ t min , t k · Ave_fill fill(k) } if fill(k)>Ave_fill ,
I B ¯ (k)= A B (k) 2 =β · T k ,W 2 =β · [ (Row · Col) · fill( T k ) ] 2 ,
ξ calibration (k)= I B ¯ (k) I B (k) =β · (Row · Col) 2 · fill ( T k ) 2 I B (k) ,
T S , T k calibration = I S (k) · ξ calibration (k)/β =(Row · Col) · fill( T k ) · I S (k) I B (k) ,
γ( T k , T S )= 2 · fill( T k ) fill( T k )+fill( T S ) · I S (k) I B (k) .
n P = n Pmin +R{ Z( min( n A , n B ), μ P , τ P )( n Pmax n Pmin ) },

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