Fingerprints are the most popular
biometrics features. Their stability
and uniqueness make the
fingerprint identification system
extremely reliable and useful for
Fingerprint details :
There are more than
120 fingerprint classes(patterns)
The five most common classes are :
• Arch: ridges enter from one side, rise to
form a small bump, then go down and to the
opposite side. No loops or delta points are
•TentedArch: similar to the arch except
that at least one ridge has high curvature, thus one
core and one delta points.
• Left loop: one or more ridges enter from
one side, curve back, and go out the same side they
entered. Core and delta are present.
• Right loop: same as the left loop,
but different direction.
• Whorl: contains at least one ridge that
makes a complete 360 degree path around the center
of the fingerprint. Two loops (same as one whole) and
two deltas can be found.
There are some unclassified fingerprints
The fingerprint recognition problem can be grouped
into three sub-domains:
The following are Fingerprint Recognition Techniques:
A. Minutiae Extraction Technique:
Most of the finger-scan technologies are based on Minutiae.
Minutia-based techniques represent the fingerprint by its
local features, like terminations and bifurcations.
Minutiae are extracted from the two fingerprints
and stored as sets of points in the 2-D plane, then the
number of points and points coordinates are compared to
B. Pattern Matching or Ridge Feature Based Techniques:
Pattern based algorithms compare the basic fingerprint
patterns (arch, whorl, and loop) between a previously stored
template and a candidate fingerprint. This requires that the
images be aligned in the same orientation. To do this, the
algorithm finds a central point in the fingerprint image and
centers on that. In a pattern-based algorithm, the template
contains the type, size, and orientation of patterns within the
aligned fingerprint image.
And There are many other
complicated techniques to
Minutiae Extraction Technique:
• the basic problem when finding consist of
deciding if the pixel evaluated belong to the
ridge or not
• Next ,we will present the processing steps in
order to adapt the input fingerprint image to
the next block requirement and to convert it
in a set of these interest lines ,named
1- Image Enhancement and noise reduction applying direction filters.
2 – Binarized the image using the Otsu method to obtain the best
3- Thinning algorithm by means of mathematical morphology for extracting
a set of interset line, obtaining the Thinned Ridge Map .
4 -Deputation of the ridge map: involves the removal of the spurious
elements and join the broken line using a smoothing procedure.
This depuration process is carried out by simple rules like :
- to remove small isolated line
- to merge all line who have end points with similar direction and the
distance between them is small
The minutiae points are determined by scanning the
local neighbourhood of each pixel in the ridge thinned image, using a
The CN value is then computed, which is defined as half the sum of the
differences between pairs of neighboring pixels pi and pi+1
minutiae are major features of a fingerprint , using which comparisons of one print with
another can be made.
* Ridge ending – the abrupt end of a ridge
* Ridge bifurcation (Fork)– a single ridge that divides into two ridges
* Island – a single small ridge inside a short ridge or ridge ending that is not connected to all
* Crossover or bridge – a short ridge that runs between two parallel ridges
* Delta – a Y-shaped ridge meeting
* Core – a U-turn in the ridge pattern