Randomized Radon Transforms for Biometric Authentication via Fingerprint Hashing

1. Randomized Radon Transforms for Biometric Authentication viaFingerprint Hashing 2007 ACM Digital Rights Management Workshop Alexandria, VA (USA) October 29, 2007 Mariusz H. Jakubowski RamarathnamVenkatesan Microsoft Research

2. Introduction • Biometrics: “What you are” • Measurements over bodily features (e.g., fingerprints) • Applications for security and convenience • Biometric hashing • One-way extraction of information from biometric data • Human identifiers for DRM authentication • Goals of our work: • New method for fingerprint hashing • Applications to strengthen and streamline DRM security

3. Overview • Introduction • Fingerprint hashing • Experimental results • Conclusion Fingerprint hashing via Radon transform

4. Fingerprint Hashing Conversion of fingerprints to one-way hashes for authentication applications • Fingerprint hash: An irreversible compressed representation of fingerprint data, extracted according to a secret key. • Basic procedure: • Compute various metrics over a fingerprint image and combine these into a hash vector. • Apply error correction and other methods to increase hash robustness.

5. Radon Transform • Standard: (x,y)  (θ, ρ), where θ and ρ denote angles and distances of lines. • Line at angle θ and distance ρ from origin will result in high value of transform coefficient (θ,ρ). • Original image • R(θ,ρ) • Hash transform: This line-based metric is replaced by a custom metric.

6. Randomizing the Transform • Standard: • Exhaustively enumerate all lines. • Typical metric: Compute projections of lines onto image. • Randomized: • Generate a pseudorandom sequence of lines, using a secret hashing key. • Simpler metric: Compute crossing counts of lines with image (i.e., number of times each line crosses or grazes fingerprint curves). • Randomized transform leads to hashing scheme.

7. Fingerprint Hashing: Example Scanned fingerprint Metric: Crossing count with random lines and curves

8. Fingerprint Hashing: Example Scanned fingerprint Cleaned fingerprint • Generic clean-up: Filters, thresholds, etc. • Specialized methods: VeriFinger (Neurotechnologija, Inc.) Metric: Crossing count with random lines and curves

9. Fingerprint Hashing: Example 5 random lines Scanned fingerprint Cleaned fingerprint Metric: Crossing count with random lines and curves

10. Fingerprint Hashing: Example 25 21 24 25 25 5 random lines Scanned fingerprint Cleaned fingerprint Metric: Crossing count with random lines and curves

11. Fingerprint Hashing: Example 25 21 24 25 25 5 random lines 22 17 21 23 23 22 22 27 24 25 14 23 25 27 25 Scanned fingerprint Cleaned fingerprint 15 random lines Metric: Crossing count with random lines and curves

12. Fingerprint Hashing: Example 25 21 24 25 25 5 random lines 22 17 21 23 23 22 22 27 24 25 14 23 25 27 25 Scanned fingerprint Cleaned fingerprint 15 random lines 3 24 44 27 32 8 16 24 37 31 Metric: Crossing count with random lines and curves Hashes (crossing counts) 10 random curves

13. Some Metrics for Hashing • Counts of crossings with lines and curves • Curvatures of fingerprint lines within random regions • Numbers and types of minutiae contained in random regions (e.g., rectangles) 7 6 0 1 2 2

14. Hash Properties • Secret key or password used to determine metric types and parameters • Controllable length and security (e.g., 64, 128, or 256 bits) • Resistance against minor scanner distortions and noise

15. Fingerprint Authentication • Standard authentication: Compare fingerprint scans against stored “correct” fingerprints. • Hash-based authentication: Compare hashes of scanned fingerprints with stored “correct” hashes. • Benefits of hashes: • Actual fingerprints need not be stored for comparison. • Stolen hashes do not reveal or compromise entire fingerprints. • Key-derived hashes bind passwords and fingerprints tightly. • Short hash length allows usage in network protocols, Web services, etc.

16. Experiments Original fingerprint Hash: 28 19 21 23 22

17. Experiments Distorted fingerprint Hash: 29 19 20 23 22 Difference: 1 0 -1 0 0 • StirMark distortions used • Approximation of real-life scanner distortions Original fingerprint Hash: 28 19 21 23 22

18. Experiments Distorted fingerprint Hash: 29 19 20 23 22 Difference: 1 0 -1 0 0 Different hash key Hash : 20 26 28 21 17 Difference: -8 7 7 -2 -5 Original fingerprint Hash: 28 19 21 23 22

19. Experiments Distorted fingerprint Hash: 29 19 20 23 22 Difference: 1 0 -1 0 0 Different hash key Hash : 20 26 28 21 17 Difference: -8 7 7 -2 -5 Original fingerprint Hash: 28 19 21 23 22 Different fingerprint #1 Hash: 38 17 24 34 28 Difference: 10 -2 3 11 6

20. Experiments Distorted fingerprint Hash: 29 19 20 23 22 Difference: 1 0 -1 0 0 Different hash key Hash : 20 26 28 21 17 Difference: -8 7 7 -2 -5 Original fingerprint Hash: 28 19 21 23 22 Different fingerprint #1 Hash: 38 17 24 34 28 Difference: 10 -2 3 11 6 Different fingerprint #2 Hash: 19 26 18 24 23 Difference: -9 7 -3 1 1

21. Experimental Results 5 random lines Distances between each fingerprint and its distorted version Distances between each fingerprint and other distinct fingerprints

22. Experimental Results 5 random lines 50 random lines Distances between each fingerprint and its distorted version Distances between each fingerprint and other distinct fingerprints

23. Experimental Results 50 random lines 200 random lines (diminishing returns) Distances between each fingerprint and its distorted version Distances between each fingerprint and other distinct fingerprints

24. Conclusion • Contributions • Methodology to extract fingerprint entropy • Applications in biometric authentication • Address “too many passwords” problem • Augment password-based schemes • Future work • Handling scanner distortions • Naturally robust metrics • Better error correction • Explicit fingerprint synchronization • Applications to other biometric data • Retinal blood vessels • Vein patterns on hands