research-article

From Template to Image: Reconstructing Fingerprints from Minutiae Points

Authors:

Anil K. JainAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 29, Issue 4

Pages 544 - 560

https://doi.org/10.1109/TPAMI.2007.1018

Published: 01 April 2007 Publication History

Abstract

Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line Integral Convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint.

References

[1]

Optel, Poland, www.optel.com.pl, 2007.

[2]

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

[3]

D.H. Ballard, “Generalizing the Hough Transform to Detect Arbitrary Shapes,” Pattern Recognition, vol. 13, no. 3, pp. 111-112, 1997.

[4]

B. Cabral and L. Leedom, “Imaging Vector Fields Using Line Integral Convolution,” Proc. 20th Ann. Conf. Computer Graphics and Interactive Techniques, pp. 263-270, 1993.

Digital Library

[5]

G. Candela, P. Grother, C. Watson, R. Wilkinson, and C. Wilson, “PCASYS—A Pattern-Level Classification Automation System for Fingerprints,” Technical Report NIST TR 5647, Aug. 1995.

[6]

R. Cappelli, A. Lumini, D. Maio, and D. Maltoni, “Fingerprint Classification by Directional Image Partitioning,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, pp. 402-421, 1999.

Digital Library

[7]

R. Cappelli, D. Maio, and D. Maltoni, “Synthetic Fingerprint Image Generation,” Proc. 15th Int'l Conf. Advances in Pattern Recognition, vol. 3, no. 5, pp. 475-478, 2000.

Digital Library

[8]

C. Champod and P. Margot, “Computer Assisted Analysis of Minutiae Occurrences on Fingerprints,” Proc. Int'l Symp. Fingerprint Detection and Identification, p. 305, 1996.

[9]

B. Cho, J. Kim, I. Bae, I. Bae, and K. Yoo, “Core-Based Fingerprint Image Classification,” Proc. 15th Int'l Conf. Pattern Recognition, vol. 2, pp. 863-866, 2000.

[10]

M. Chong, T. Ngee, L. Jun, and R. Gay, “Geometric Framework for Fingerprint Image Classification,” Pattern Recognition, vol. 30, no. 9, pp. 1475-1488, 1997.

[11]

F. Galton, Finger Prints. McMillan, 1892.

[12]

I.B. Group, “Generating Images from Templates,” technical report, I.B.G. white paper, 2002.

[13]

E. Henry, Classification and Uses of Fingerprints. Routledge, 1900.

[14]

C. Hill, “Risk of Masquerade Arising from the Storage of Biometrics,” master's thesis, Australian Nat'l Univ., 2001.

[15]

L. Hong and A. Jain, “Classification of Fingerprint Images,” Proc. 11th Scandinavian Conf. Image Analysis, June 1999.

[16]

L. Hong, Y. Wan, and A.K. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evaluation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777-789, Aug. 1998.

Digital Library

[17]

A. Jain, S. Prabhakar, and L. Hong, “A Multichannel Approach to Fingerprint Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, pp. 348-359, 1999.

Digital Library

[18]

A.K. Jain and S. Minut, “Hierarchical Kernel Fitting for Fingerprint Classification and Alignment,” Proc. 16th Int'l Conf. Pattern Recognition, vol. 2, pp. 469-473, Aug. 2002.

[19]

B. Jobard and R. Wilfrid, “Creating Evenly-Spaced Streamlines of Arbitrary Density,” Proc. Eurographics Workshop Visualization in Scientific Computing, 1997.

[20]

K. Karu and A. Jain, “Fingerprint Classification,” Pattern Recognition, vol. 29, pp. 389-404, 1996.

[21]

C. Kingston, “Probabilistic Analysis of Partial Fingerprint Patterns,” PhD thesis, Univ. of California, Berkeley, 1964.

[22]

D. Laidlaw, R. Kirby, C. Jackson, J. Davidson, T. Miller, M. Silva, W. Warren, and M. Tarr, “Comparing 2D Vector Field Visualization Methods: A User Study,” IEEE Trans. Visualization and Computer Graphics, vol. 11, no. 1, pp. 59-70, Jan./Feb. 2005.

Digital Library

[23]

D. Maltoni, D. Maio, A.K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, first ed. Springer-Verlag, 2003.

Digital Library

[24]

A. Moenssens, Fingerprint Techniques. Chilton Book Company, 1971.

[25]

H. Moon and P. Phillips, “Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms,” Perception, vol. 30, pp. 303-321, 2001.

[26]

N. Ratha, S. Chen, and A. Jain, “Adaptive Flow Orientation-Based Feature Extraction in Fingerprint Images,” Pattern Recognition, vol. 28, no. 11, pp. 1657-1672, 1995.

[27]

A. Ross, J. Shah, and A. Jain, “Towards Reconstructing Fingerprints from Minutiae Points,” Proc. SPIE Conf. Biometric Technology for Human Identification II, pp. 68-80, 2005.

[28]

T. Roxburgh, “On the Evidential Value of Fingerprints,” Sankhya: Indian J. Statistics, pp. 189-214, 1993.

[29]

A. Sadarjoen, T. Walsum, A. Hin, and F. Post, “Particle Tracing Algorithms for 3D Curvilinear Grids,” Proc. Fifth Eurographics Workshop Visualization and Scientific Computing, 1994.

Digital Library

[30]

A. Senior, “A Combination Fingerprint Classifier,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 10, pp. 1165-1174, Oct. 2001.

Digital Library

[31]

B. Sherlock and D. Monro, “A Model for Interpreting Fingerprint Topology,” Pattern Recognition, vol. 26, no. 7, pp. 1047-1055, 1993.

[32]

D.A. Stoney, “Quantitative Assessment of Fingerprint Individuality,” PhD thesis, Univ. of California, Davis, 1985.

[33]

V. Verma, D. Kao, and A. Pang, “A Flow-Guided Streamline Seeding Strategy,” Proc. Conf. Visualization, pp. 163-170, 2000.

Digital Library

[34]

C. Wilson, G. Candela, and C. Watson, “Neural Network Fingerprint Classification,” J. Artificial Neural Networks, vol. 1, no. 2, pp. 203-228, 1994.

[35]

Y. Yao, P. Frasconi, and M. Pontil, “Fingerprint Classification with Combination of Support Vector Machines,” Proc. Third Int'l Conf. Audio- and Video-Based Biometric Person Authentication, pp. 253-258, 2001.

Digital Library

Cited By

Bangalore Narasimha Prasad MMakrushin AFerrara MKraetzer CDittmann JP�rez-Gonz�lez FComesa�a-Alfaro PKr�tzer CVicky Zhao H(2024)GAN-based Minutiae-driven Fingerprint MorphingProceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security10.1145/3658664.3659632(175-186)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3658664.3659632
Lin KChen Y(2024)A High-security-level Iris Cryptosystem Based on Fuzzy Commitment and Soft Reliability ExtractionIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.328991621:4(1770-1784)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TDSC.2023.3289916
Lin KChen Y(2023)Advanced Research on High-security-level Error-correction-based Iris Recognition SystemProceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering10.1145/3637732.3637777(59-66)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3637732.3637777
Show More Cited By

Index Terms

From Template to Image: Reconstructing Fingerprints from Minutiae Points
1. Computing methodologies

Recommendations

FM Model Based Fingerprint Reconstruction from Minutiae Template
ICB '09: Proceedings of the Third International Conference on Advances in Biometrics

Minutiae-based representation is the most widely adopted fingerprint representation scheme. The compactness of minutiae template has created an impression that the minutiae template does not contain sufficient information to allow the reconstruction of ...
Fingerprint Reconstruction: From Minutiae to Phase

Fingerprint matching systems generally use four types of representation schemes: grayscale image, phase image, skeleton image, and minutiae, among which minutiae-based representation is the most widely adopted one. The compactness of minutiae ...
Direct Gray-Scale Minutiae Detection In Fingerprints

Most automatic systems for fingerprint comparison are based on minutiae matching. Minutiae are essentially terminations and bifurcations of the ridge lines that constitute a fingerprint pattern. Automatic minutiae detection is an extremely critical ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 29, Issue 4

April 2007

240 pages

ISSN:0162-8828

Issue’s Table of Contents

Copyright © 2007.

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 April 2007

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Bangalore Narasimha Prasad MMakrushin AFerrara MKraetzer CDittmann JP�rez-Gonz�lez FComesa�a-Alfaro PKr�tzer CVicky Zhao H(2024)GAN-based Minutiae-driven Fingerprint MorphingProceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security10.1145/3658664.3659632(175-186)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3658664.3659632
Lin KChen Y(2024)A High-security-level Iris Cryptosystem Based on Fuzzy Commitment and Soft Reliability ExtractionIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.328991621:4(1770-1784)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TDSC.2023.3289916
Lin KChen Y(2023)Advanced Research on High-security-level Error-correction-based Iris Recognition SystemProceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering10.1145/3637732.3637777(59-66)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3637732.3637777
Baghel VPrakash S(2023)Generation of Secure Fingerprint Template Using DFT for Consumer Electronics DevicesIEEE Transactions on Consumer Electronics10.1109/TCE.2022.321723469:2(118-127)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TCE.2022.3217234
Musto RKuzu RMaiorana EHine GCampisi P(2023)Learning Biometric Representations with Mutually Independent Features Using Convolutional AutoencodersSN Computer Science10.1007/s42979-023-01974-z4:5Online publication date: 14-Aug-2023
https://dl.acm.org/doi/10.1007/s42979-023-01974-z
Ali ABaghel VPrakash S(2023)A novel technique for fingerprint template security in biometric authentication systemsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-022-02726-539:12(6249-6263)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00371-022-02726-5
S P RThomas T(2022)Cancelable biometric scheme based on dynamic salting of random patchesMultimedia Tools and Applications10.1007/s11042-022-13764-582:10(14337-14366)Online publication date: 15-Sep-2022
https://dl.acm.org/doi/10.1007/s11042-022-13764-5
Baghel VAli APrakash S(2022)A robust and singular point independent fingerprint shellApplied Intelligence10.1007/s10489-022-04038-653:8(9270-9284)Online publication date: 8-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-022-04038-6
Keller DOsadchy MDunkelman O(2021)Inverting Binarizations of Facial Templates Produced by Deep Learning (and Its Implications)IEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.310305616(4184-4196)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIFS.2021.3103056
Baghel VPrakash SAgrawal I(2021)An enhanced fuzzy vault to secure the fingerprint templatesMultimedia Tools and Applications10.1007/s11042-021-11325-w80:21-23(33055-33073)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s11042-021-11325-w
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents