This document presents a method for removing gender signatures from fingerprints to protect privacy. The method filters fingerprint images in the frequency domain to diminish patterns that reveal gender. Testing showed the method reduced initial gender estimation accuracy from 88.7% to 50.5% while not significantly impacting matching performance. The method provides an effective way to de-identify fingerprints and address privacy concerns related to automatic gender estimation from biometrics.

1. Removing Gender Signature
from Fingerprints
Luca Lugini, Emanuela Marasco, Bojan Cukic, Jeremy Dawson
Lane Department of Computer Science and Electrical Engineering
Biometrics & Forensics & De-identification and Privacy Protection
May 29th 2014, Opatija
1West Virginia University

2. Outline
• Privacy Issue
• Automatic gender estimation
• Gender de-identification
• Results
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3. Problem
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Privacy Protection
• Fingerprints may reveal valuable secondary information: age /
gender, and other characteristics [1] [2].
• Possibly beneficial for forensic applications, but undesirable for
most biometric use cases.
Goal:
Obfuscation of age / gender through fingerprint image de-
identification, without degrading match accuracy.
[1] E. Marasco, L. Lugini, and B. Cukic, “Exploiting Quality and Texture features to Estimate Age and Gender through Fingerprint
Images”, SPIE Defense and Security, pp. 1–10, 2014.
[2] P. Gnanasivam, and S. Muttan, “Fingerprint Gender Classification using Wavelet Transform and Singular Value Decomposition”
International Journal of Biometrics and Bioinformatics (IJBB) 9(2) (2012).
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4. Related Work
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Literature on Gender Classification from Fingerprints
De-Identification Literature
• Manual Method (2006) [1]:
Ridge count, Ridge thickness to valley thickness ratio, white lines count, pattern type concordance
Dataset: 1100 males, 1100 females
Results: GAR=88.28% Neural Network, GAR=86.5% LDA, GAR=80.39% Fuzzy C-Mean
• Automated Method (2012) [2]:
Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD)
Dataset: 1980 males, 1590 females; age groups: up to 12, 13-19, 20-25, 26-35, 36 and above
Results: GAR=88.28%
For Faces: K-Same algorithm [3] & Extension of the K-Same algorithm [4]
• From face similarities an algorithm creates new faces.
• It guarantees privacy when sharing video data by preserving facial details but ensuring a low
reliability of face recognition
• The extension takes into account linear appearance variations of faces
[1] Badawi, A., Mahfouz, M., Tadross, R., & Jantz, R. (2006, June). Fingerprint-Based Gender Classification. In IPCV (pp. 41-46)
[2] Gnanasivam, P., S. Muttan. "Fingerprint Gender Classification using Wavelet Transform and Singular Value Decomposition”, IEEE-2012
[3] E. Newton, L. Sweeney, B. Malin, “Preserving Privacy by De-Identifying Face Images,” IEEE Transactions on Knowledge and Data Engineering, vol.
17, no. 2, pp. 232–243, 2005.
[4] R. Gross, L. Sweeney, J. Cohn, F. de la Torre, and S. Baker, “Face De-Identification,” Protecting Privacy in Video Surveillance, pp. 129–146, 2009.

5. The Proposed Approach
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• Ad-hoc image filtering and scaling in the frequency domain
diminishes gender patterns.
• The distortion is introduced in both the gallery and probe
fingerprints.

6. Automatic Gender Estimation
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E. Marasco, L. Lugini, and B. Cukic, “Exploiting Quality and Texture features to Estimate Age and Gender through
Fingerprint Images”, SPIE Defense and Security, pp. 1–10, 2014.
• Impact of Gender on Matching and Image Quality
• NFIQ
• Energy Concentration
• Histogram of LBP
• Histogram of LPQ
• Entropy
• Energy Distributions
• Gender Estimation Results
10-Fold Cross Validation

7. De-Identification Algorithm
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Input:
Let I(x,y) be the original fingerprint image.
Let B be the number of frequency bands.
Let w0 and w1 represent the male and female classes, respectively.
Output:
DI(x,y) de-identified image.
1. Compute the Discrete Fourier Transform F(u,v) of the image I(x,y).
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8. De-Identification Algorithm
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2. Filter the image in the frequency domain:
Gk(u,v) = Hk(u,v)F(u,v); for k = 1,…B
3. Estimate the energy distributions for w0 and w1:
Ek,w0 = |Gk(u,v)|2
Ek,w1 = |Gk(u,v)|2; for k = 1,…B
4. Compute scaling parameters ak, for k = 1,…B
µk,w0 = mean(Ek,w0)
µk,w1 = mean(Ek,w1)
ak = (µk,w0 - µk,w1)/2
5. Apply the scaling in the frequency domain:
If w1 F*(u,v) = (ak + 1) F(u,v)
else F*(u,v) = ak F(u,v)
6. Compute the inverse of F(u,v)

9. Dataset
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• Data collection performed at West Virginia University
• FBI Certified livescan fingerprint systems
• Number of participants: 500
• Two sequential sessions of fingerprints for each sensor
• Rolled individual fingerprints on right and left hands; left, right
and thumb slaps per session
– In the analysis we use right point finger only

10. Visual Results
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Female subject
before and after de-identification
Male subject
before and after de-identification
• Visually, the impact of de-identification process on the fingerprint
images is not pronounced

11. Gender Estimation after De-Identification
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Energy distributions of specific frequency bands after de-identification
• Frequency components in original images separate females from males well
• Corresponding frequency components in de-identified images for males and
females overlap
• The initial gender estimation accuracy of 88.7% before de-identification is
reduced to 50.5%
Energy distributions of specific frequency bands before de-identification

12. Matching De-Identified Images
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Verification Performance before and after De-Identification
• Variations induced in the images do not drastically affect verification
error rates, as expected from an effective de-identification algorithm

13. Conclusions
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• The initial gender estimation accuracy of 88.7% before de-
identification is reduced to 50.5%
• Visually, the impact of de-identification process on the fingerprint
images is not pronounced
• Variations induced in the images do not drastically affect verification
error rates, as expected from an effective de-identification algorithm
• We propose a new de-identification algorithm to remove gender
signature from fingerprints
• Automatic estimation of gender from fingerprints arises concerns
about privacy protection

14. West Virginia University
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Any Questions?
Thanks for your Attention!
emanuela.marasco@mail.wvu.edu
Phone: (304) 293-1455
Emanuela Marasco, Ph.D.
WVU CITeR
Statler College of Engineering and Mineral Resources
LCSEE – PO Box 6109
395 Evansdale Drive, ESB Annex 171
Morgantown WV 26506 USA