Touchless fingerprint
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1) Touchless fingerprint systems use remote sensing to capture fingerprints without physical contact, avoiding issues like skin distortion, slippage, and residue seen in touch-based systems. They allow for larger capture areas and higher user comfort. 2) 3D touchless fingerprint systems acquire full fingerprint images from multiple views and extract a 3D shape of the finger. The 3D print can then be unwrapped onto a 2D plane either through parametric modeling like cylindrical projection or non-parametric direct unfolding. 3) Key steps in fingerprint recognition systems using touchless scans include image acquisition, preprocessing, feature extraction like finding minutiae, and matching against an existing database.
Touchless fingerprint
- 1. Presented by Shree Prakash M.Tech(R) 611CS106
- 2. INTRODUCTION • Fingerprint is most popular,reliable and oldest biometric sign of identity • Touchless fingerprint system is a remote sensing technique to process fingerprint pattern, considered as a viable alternative to touch based fingerprint system • New generation of touchless live scan devices is 3D touchless finger print system
- 3. FINGERPRINT PATTERN 1) ARCH (a) Plain (b)Tented 2) LOOP (c)Left (d)Right (e)Twin 3) WHORL
- 4. TERMINOLOGY • Fingerprint can be looked at from different levels 1) GLOBAL LEVEL • Singularity points called core and delta points Core and delta points marked on sketches of the two fingerprint patterns loop and whorl
- 5. 2) LOCAL LEVEL Minutiae details in terms of ridges Ridge bifurcation Ridge termination Representation of minutiae
- 6. 3) VERY FINE LEVEL Finger sweat pores
- 7. TOUCHLESS VERSUS TOUCH-BASED TOUCHLESS TOUCH-BASED SKIN DISTORTION NO YES SLIPPAGE,SMEARING NO YES FINGERPRINT RESIDUE NO YES CAPTURE AREA LARGE SMALL TOLERANCE ON SKIN CONDITION LARGE SMALL HIGH LOW USER COMFORT LEVEL
- 8. FINGERPRINT RECOGNITION SYSTEM A. IMAGE ACQUISITION B. PREPROCESSING C. FEATURE EXTRACTION D. MATCHING
- 9. IMAGE ACQUISITION MULTIPLE VIEW SYSTEM Figure 1. Fingerprint acquisition using a set of cameras surrounding the finger
- 10. • Multiple view enables the capture of full nail to nail fingerprints increasing the usable area • From each acquired image a silhouette is extracted. • The 5 silhouettes are then projected into the 3D space and we get the 3D shape of finger by knowing the position and orientation of each camera within a reference coordinate system.
- 11. Figure 2 Fingerprint acquisition obtained by combining a single line scan camera and two mirrors
- 12. 3D FINGERPRINT UNWRAPPING • Unwrapping a 3D object refers to the unfolding the 3D object onto a flat 2D plane. UNWRAPPING METHOD PARAMETRIC NON PARAMETRIC
- 13. PARAMETRIC UNWRAPPING USING CYLINDRICAL MODEL • Human fingers vary in shape, like the shape of the middle finger is often more cylindrical than the thumb. • Human fingers can be closely approximated by cylinders. • Human fingers also vary in size and the cylindrical model can also capture this size variability in the vertical direction, but not in the horizontal direction. • Cylindrical model is a reasonable choice for parametric unwrapping of3D fingerprints.
- 14. T Parametric unwrapping using a cylindrical model (top down view). Point (x,y,z) on the 3D finger is projected Figure 3 to ( ,z) on the 2D plane.
- 15. • Mathematically, let the origin be positioned at the bottom of the finger, centered at the principle axis of the finger. • Let T be a point on the surface of the 3D finger: x T = Y z
- 16. • This 3D point is then projected (transformed) onto the cylindrical surface to obtain the corresponding 2D coordinates S = z Where
- 17. NON PARAMETRIC -UNWRAPPING • Non-parametric unwrapping, does not involve any projection on parametric models. • The unwrapping directly applies to the object to preserve local distances or angular relations. • Guarantees the variability in both shape and size of fingers is preserved. • Less distortion compared to parametric unwrapping
- 18. COMPARISION PARAMETRIC UNWRAPPING UNPARAMETIC WNRAPPING
- 19. PREPROCESSING STEPS a) Computation of local ridge frequency and local ridge orientation b) Enhancement of the fingerprint image c) Segmentation d) Detection of singularities FEATURE EXTRACTION a) Conversion of preprocessed fingerprint image into binary image b) Thinning
- 20. FINGERPRINT MATCHING • MINUTIAE-BASED APPROACH :- Analogous with the way that forensic experts compare fingerprints • The minutiae sets of the two fingerprints to be compared are first aligned, requiring displacement and rotation to be computed • Region of tolerance around the minutiae position is defined in order to compensate for the variations that may appear in the minutiae position due to noise and distortion
- 21. DISADVANTAGE • Lower contrast between ridges and valleys due to motion blur of hand tremble , camera background noise and small depth of field • Unwrapping technique has distortion upto some extent • Compatibility with contact-based 2D rolled fingerprint image
- 22. REFERENCE • Intelligent biometrics technique in finger print and face recognition by L.C Jain, U.halice, S.B lee,S.T Sutsui,I.Hayashi • Tabassi E., Wilson C., and Watson C., “Fingerprint Image Quality,” Tech. Rep. 7151, National Institute of Standards and Technology (NIST), August 2004. • Parziale G. and Diaz-Santana E., “The Surround Imager: Multi-Camera Touchless Device to Acquire 3D Rolled- Equivalent Fingerprints,” in Proceedings of IAPR International Conference on Biometrics (ICB),Hong Kong, China, January 2006, pp. 244–250.