Fingerprint Synthesis

An Hong Tran

Outline

Introduction Haar Wavelet Transform Fingerprint Synthesis Application Results Conclusion

Introduction

Verification Test Large Database

Use Parameter Varying techniques

New approach Extract features from parents

Haar Wavelet Transform

F C

D

FDecomposition Reconstruction

Haar Wavelet Transform

Fingerprint Synthesis Application

Direct (pixel value)

Orientation map (O-map) switching

Orientation map (O-map) manipulation

Orientation map (O-map) merging

Fingerprint Synthesis Application

Direct (pixel value)

O-map switching

O-map manipulation

O-map merging

Fingerprint Synthesis Application – Direct

(a)

(b)

Fingerprint Synthesis Application – Direct

Fingerprint Synthesis Application – Direct

Fingerprint Synthesis Application

Direct (pixel value)

O-map Switching

O-map manipulation

O-map merging

Fingerprint Synthesis Application – O-map switch

Fingerprint Synthesis Application – O-map switch

Fingerprint Synthesis Application – O-map switch

Fingerprint Synthesis Application

Direct (pixel value)

O-map switching

O-map manipulation

O-map merging

Fingerprint Synthesis Application – O-map manip

Fingerprint Synthesis Application – O-map manip

Fingerprint Synthesis Application – O-map manip

Fingerprint Synthesis Application – O-map manip

Fingerprint Synthesis Application – O-map manip

Fingerprint Synthesis Application

Direct (pixel value)

O-map switching

O-map manipulation

O-map merging

Fingerprint Synthesis Application – O-map merge

Fingerprint Synthesis Application – O-map merge

Fingerprint Synthesis Application – O-map merge

Fingerprint Synthesis Application – O-map merge

Fingerprint Synthesis Application – O-map merge

Result

Nnew = N + 3Σk=2 NCk ,

Nnew is the number of sample in the new database, N is number of original samples

NCk is the Combination operation, 6C2 = 15.

Nnew = N + 3(2n – 1)

Conclusion & Future Work

Show good promises. Merging features from parents.

Weight Vector analysis. Balance between uniqueness and

realism.

References

1. Local B-spline Multiresolution with Examples in Iris Synthesis and Volumetric Rendering, Faramarz F. Samavati, Richard .H. Bartels and Luke Olsen, Image Pattern Recognition: Synthesis and Analysis in Biometrics, Series in Machine Perception and Artificial Intelligence , Vol. 67, World Scientific Publishing, 2007.

2. A. Adler, “Can Images Be Regenerated from Biometric Templates,” Proc. Biometrics Consortium Conf., Sept. 2003.

3. R. Cappelli, “Synthetic Fingerprint Generation,” Handbook of Fingerprint Recognition, D. Maltoni, D. Maio, A.K. Jain, and S. Prabhakar, eds., Springer, 2003.

4. Anil K. Jain , David Maltoni, Handbook of Fingerprint Recognition, Springer-Verlag New York, Inc., Secaucus, NJ, 2003

5. B. Sherlock and D. Monro, “A Model for Interpreting Fingerprint Topology”, in Pattern Recognition, v. 26, no. 7, 1993, pp. 1047-1095.

6. P. Vizcaya and L. Gerhardt, “A Nonlinear Orientation Model for Global Description of Fingerprints”, in Pattern Recognition, V29, no. 7, 1996, pp. 1221-1231.

7. S. Yanushkevich, V. Shmerko, and D. Popel, Biometric Inverse Problem, CRC Press, 2005.