Randomized Radon Transforms for Biometric Authentication via Fingerprint Hashing

Randomized Radon Transforms for Biometric Authentication via

Fingerprint Hashing

2007 ACM Digital Rights Management WorkshopAlexandria, VA (USA)

October 29, 2007

Mariusz H. JakubowskiRamarathnam Venkatesan

Microsoft Research

2007 ACM Digital Rights Management Workshop October 29, 2007 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


Overview

• Introduction• Fingerprint hashing• Experimental results• Conclusion

Fingerprint hashing via Radon transform


Fingerprint HashingConversion 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.


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).

Hash transform: This line-based metric is replaced by a custom metric.

R(θ, ρ)Original image


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.


Fingerprint Hashing: Example

Scanned fingerprint

Metric: Crossing count with random lines and curves



Scanned fingerprint


Cleaned fingerprinto Generic clean-up: Filters, thresholds, etc.o Specialized methods: VeriFinger

(Neurotechnologija, Inc.)



Scanned fingerprint

5 random lines


Cleaned fingerprint



Scanned fingerprint

25 21 24 25 25

5 random lines


Cleaned fingerprint



Scanned fingerprint

25 21 24 25 25

22 17 21 23 2322 22 27 24 2514 23 25 27 25

5 random lines

15 random lines


Cleaned fingerprint



Scanned fingerprint

25 21 24 25 25

22 17 21 23 2322 22 27 24 2514 23 25 27 25

5 random lines

15 random lines

Metric: Crossing count with random lines and curves 10 random curves

Cleaned fingerprint

3 24 44 27 328 16 24 37 31

Hashes (crossing counts)


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


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


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.


Experiments

Original fingerprintHash: 28 19 21 23 22


Experiments


Distorted fingerprintHash: 29 19 20 23 22Difference: 1 0 -1 0 0

o StirMark distortions usedo Approximation of real-life scanner distortions


Experiments



Different hash keyHash : 20 26 28 21 17Difference: -8 7 7 -2 -5


Experiments


Different fingerprint #1Hash: 38 17 24 34 28Difference: 10 -2 3 11 6




Experiments


Different fingerprint #1Hash: 38 17 24 34 28Difference: 10 -2 3 11 6

Different fingerprint #2Hash: 19 26 18 24 23Difference: -9 7 -3 1 1




Experimental Results

0 5 10 15 20 250

10

20

30

40

50

60

70

80

90

Fingerprint Number

Dis

tanc

e

Distances between each fingerprint and its distorted version

Distances between each fingerprint and other distinct fingerprints

5 random lines



0 5 10 15 20 250

10

20

30

40

50

60

70

80

90

Fingerprint Number

Dis

tanc

e

0 5 10 15 20 250

100

200

300

400

500

600

Fingerprint NumberD

ista

nce



5 random lines 50 random lines



0 5 10 15 20 250

100

200

300

400

500

600

Fingerprint Number

Dis

tanc

e



50 random lines 200 random lines(diminishing returns)

0 5 10 15 20 250

500

1000

1500

2000

2500

Fingerprint NumberD

ista

nce


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

Documents

Randomized Radon Transforms for Biometric Authentication via Fingerprint Hashing