1. CRYPTO KEY GENERATION USING SLICING WINDOW ALGORITHM M.S. Altarawneh, L.C. Khor, W.L. Woo, and S.S. Dlay School of Electrical, Electronic and Computer Engineering University of Newcastle Newcastle upon Tyne, NE1 7RU UNITED KINGDOM Email: {mokhled.al-tarawneh, l.c.khor, w.l.woos. s.dlay} @ncl.ac.uk{mokhled.al-tarawneh, l.c.khor, w.l.woos. s.dlay} @ncl.ac.uk

2. Abstract Background  Fingerprint minutiae points are used for generating cryptographic key.  Slicing window partitioning formation on base Euclidean distance between detected core and minutiae points.  Generated vector used to derive an encryption key. Challenges  Security at your fingertips.  Bio-Crypto Key Generation.  However, approach has to be validated based on consistency to avoid false positives: relatively little work done in the field of cryptographic key- generation. Contributions  A novel approach to generate encryption key from fingerprint sample is introduced.  Experimental analysis show encouraging prospects.

3. Outline  Introduction  Prior related work  Proposed Approach  Mathematical representation of RP detecting algorithm  Slicing window construction  Key generation  Experimental Evaluation  Conclusion

4. Introduction Using biometric data as a basis for cryptographic keys is problematic:  biometric measurement is not perfectly reproducible.  cryptography relies on a stable and unique key to encrypt and decrypt texts. Incorporation address approaches of biometric - cryptography :  Key release algorithms.  key generation algorithms.

5. Proposed Approach Slicing Window Construction Reference Point Detection Minutiae Extraction Key Generation SW<=MA S YES NO

6. Proposed Approach Orientation tensor image field computation: Reference Point Detection where and denote the derivatives of the image in x and y direction respectively Complex filter computation: Where represents filter order and is standard deviation of modulated filter which is in this case modulated by Gaussian envelope Mathematical representation of reference point (RP) detecting algorithm.

7. applying Conditional/ Crossing Number (CN) concept, CN extracts the ridge points from the skeleton image. Reference Point Detection Minutiae Extraction Where is the pixel belonging to the neighbourhood of Extracted minutiae points contain: Where is the x-coordinate position, is the y-coordinate position, is the type and distance of a particular minutiae.

8. Slicing Window Construction Reference Point Detection Minutiae Extraction SW<=MA S YES RP and minutiae points distance determined by Euclidean distance form: Where is the reference point coordination and is the minutiae point coordination. xytd 9124685.094 14182296.519 24115271.197 24182288.408 28144667.912 30152268.352 34246120.07 For i=1: T; // T is template size Window size=64x64 Do minutiae counting entire window; Vector generating; // number of minutiae by window size Next windows; // 128… 256, till end of template size End Minutiae point coordination's and slicing window algorithm

9. Slicing Window Construction Reference Point Detection Minutiae Extraction SW<=MA S YES Window construction on base of template information.

10. Slicing Window Construction Reference Point Detection Minutiae Extraction Key Generation SW<=MA S YES NO vector generation:  V=slicing window size * minutiae points’ quantity  V={Header locker key and Encryption provider key }  Header locker key (HLK) will be produced by V1, V3 concatenating.  Encryption provider key (EPK) by V2, V4 concatenating. Example of generated Vector HLK EPK

11. Experimental Analyses Test environment is: 400 fingerprints images database (TIFF, format, 300x300 sizes, and 500 dpi resolutions).  Tests show that generated key length is depend on: extracted minutiae points and their positions in slicing windows.  Test shows 100% uniqueness of generated keys. Resistance brute force attacks of our approach is increased by:  Entropy of applicable system feed by HLK and EPK.  Two secure circles, cipher header closing and plain text encoding.

12. Conclusion Our approach takes advantage of fingerprint template extracted information and standard encryption algorithms to provide a novel way of generating cipher keys without having to remember complicated sequences which might be lost, stolen, or even guessed.

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14. Thank You