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.