Fingerprint Minutiae
as well as reliable and user friendly. Biometric techniques used fingerprint
for automatic personal identification and verification and fingerprint science
is the simplest and cheapest method for identification and no question on its
reliability. As all of us know that fingerprint science is
based on two fundamentals -
Permanency and Individuality.
Every individual has unique fingerprints and they remain permanent. Fingerprints
at scene of crime link the criminal to the crime/crime scene. Now a day for matching
of chance print with data base prints AFIS system (Automated Fingerprint Identification Systems)
is used and it is based on the
ridge characteristics known as minutiae.
Minutiae are the points where the ridge lines end or fork/split. The major features
of a fingerprint image are the minutiae points and they are used in the matching
of fingerprints. The uniqueness of a fingerprint image is determined by minutiae
points. Minutiae points are of many types.
These types include –
Ridge ending, ridge bifurcation, ridge dots, enclosure (lakes), short ridge, bridges,
ridge crossing, hook (spur).
Ridge ending is defined as a point where the ridge ends
suddenly.
Ridge bifurcation is defined as a point where a single ridge
branches divide into two or more ridges.
An isolated ridge unit whose length approximates its width
in size is ridge dot.
Enclosures are a single friction ridge that bifurcates and rejoins after a short course and continues as a
single friction ridge.
Spurs is a notch protruding from a side ridge towards the
other ridge.
A connecting friction ridge between parallel running ridges,
generally right angle.
Crossovers are formed when two ridges cross each other and
intersect.
The most commonly used minutia types are Ridge endings and ridge bifurcations, all other types of
minutiae are based on a combination of these two types.
Minutiae based fingerprint recognition
Minutiae based fingerprint recognition is more accurate in
comparison to other correlation based systems. When two fingerprints match in
the system it is said if their minutiae points match. This minutiae based
technique is the backbone of currently available fingerprint recognition systems.
In fingerprint identification system, the captured
fingerprint image/chance prints needs to be matched against the stored
fingerprint database. This involves a lot of computation and search overhead
and for this we need a system which restricts the size of the templates
database. To achieve this, we extract the minutiae features of the fingerprint
and it reduces the complex issue of fingerprint recognition to an issue of
point pattern matching.
Input fingerprint images quality is the key point in the
performance of a minutiae extraction algorithm, therefore fingerprint
enhancement algorithm is very essential in the minutiae extraction module.
Acquired fingerprint images due to some variations in
impression conditions, ridge configuration, skin conditions, scars, dirt,
humidity, acquisition devices etc. is significantly of poor quality. The ridge characteristics
in poor-quality fingerprint images are not well-defined and cannot be correctly
detected. Problems arise due to this is…
Spurious minutiae may be created,
Some genuine minutiae may be ignored, and
Errors in the position and orientation of minutiae may be
introduced.
So an enhancement algorithm that increases the clearness of
the ridge structures is necessary.
We have variety of techniques for extracting fingerprint
minutiae and are classified into two types –
techniques that work on binarized images and
techniques that work on gray scale images.
Three levels of fingerprint features in a fingerprint image is –
Singular points and global ridge patterns, e.g., deltas and
cores are level-1 features; these are the macro details of fingerprints. They
are used for fingerprint classification rather than recognition. Level-2
features are minutiae (ridge endings and bifurcations). They are the
distinctive and stable features, used in automated fingerprint recognition
systems and are extracted from low-resolution fingerprint images (~500 dpi). Resolution
of 500 dpi is the standard fingerprint resolution. Level 3 features include
sweat pores, ridge contours, and ridge edge features, which provide
quantitative data supporting correct and robust fingerprint recognition.
How many minutiae points are needed to match two fingerprint or to say them identical?
MR. DEEPAK PAWAR has been working as Finger Print Expert / Police Officer in Finger Print Bureau , Delhi Police at New Delhi since 2001 . He is the senior most Finger Print Expert , attending / visited several scene of crime and solved many crime cases .He has passed M.Sc (Chemistry) and he is the Member of International Association of Identification . He is the Member of Delhi Police welfare association . He is supervising the Forensic , New Delhi District , Mobile Crime Team . He is one of the Expert in the Special Investigation Team held for investigation of important cases . He has been giving fingerprint proficient training to Delhi Police personnel's . He has been Presented / giving lectures on topics related to forensic science . He is also making examination paper of Finger Print proficient course . He is awarded with Police Medal by MHA and Delhi Police.