21-08-2013, 04:01 PM
Touch-less Fingerprint Analysis — A Review and Comparison
Fingerprint Analysis.pdf (Size: 1,019.66 KB / Downloads: 41)
Abstract
Touch-less fingerprint recognition system is
a reliable alternative to conventional touch-based
fingerprint recognition system. Touch-less system is
different from conventional system in the sense that
they make use of digital camera to acquire the
fingerprint image where as conventional system uses
live-acquisition techniques. The conventional
fingerprint systems are simple but they suffer from
various problems such as hygienic, maintenance and
latent fingerprints. In this paper we present a review of
touch-less fingerprint recognition systems that use
digital camera. We present some challenging problems
that occur while developing the touch-less system.
These problems are low contrast between the ridge and
the valley pattern on fingerprint image, non-uniform
lighting, motion blurriness and defocus, due to less
depth of field of digital camera. The touch-less
fingerprint recognition system can be divided into three
main modules: preprocessing, feature extraction and
matching. Preprocessing is an important step prior to
fingerprint feature extraction and matching.
Introduction
Fingerprint recognition system is a biometric system
that uses fingerprint as biometric input to this system. A
fingerprint consists of patterns of ridges and valleys on
the surface of a fingertip. Each individual has
fingerprint which is different from the other. Actually
this biometric system is a computer vision system
which performs following functions: Image acquisition
processing or verification or matching. Basically,
Fingerprint recognition system is an identification
system that can be an Automated Fingerprint
Identification System (AFIS) or a Non-automated
Fingerprint Recognition System. Earlier, we used to
take fingerprints using “ink techniques” in which black
ink is spread on fingertip and it is pressed against a
paper card, it is also called as “off-line fingerprint
acquisition technique”[1]. This technique is used in the
law enforcement to acquire criminal’s fingerprints.
Nowadays, live-scan acquisition technique is used in
civil and criminal AFIS (Automated Fingerprint
Identification system), that make use of sensors like
optical, solid-state to acquire fingerprints.
Disadvantages of Touch Based Sensors
While using the touch-based sensor interface, the
fingertip needs to be placed over the interface so that a
proper fingerprint image can be taken. But the touch-
based sensors have several problems like the problem of
contamination which occurs because of placing the
fingertip over the same interface which is already used
by other. This produces a low quality fingerprint image.
Another problem is due to contact pressure, which
creates physical distortions which are usually non-linear
in arbitrary direction and strength. Moreover, the
distortion occurs globally, while its deformation
parameters could be different locally in a single
fingerprint image [2]. Fig 1 shows the fingerprint image
of one fingertip but with different minutiae because of
physical pressure [2].
Pre-Processing
Preprocessing is an important step prior to fingerprint
feature extraction and matching. As the fingerprint
images are captured using digital camera which had
certain challenging problems as stated earlier so, these
fingerprints require more preprocessing over them. Pre-
processing is divided into four blocks.
Core point Detection
To differentiate the entries of fingerprint images
singular points, SPs are used. SPs are points that can be
consistently detected in a fingerprint image and can be
used as a registration point. Typically there are two
types of singular points: core point and delta point. In
this paper we only proposed the core point detection
method. Fingerprint’s core point can be defined as the
point of maximum curvature in the fingerprint image.
Conclusion
In this paper, a review of touch-less fingerprint
recognition system, which can be an automated or a
biometric digital camera based system is presented. We
also presented number of comparisons between touch-
less systems and the conventional fingerprint
recognition systems. Further, this paper presented a
modeled system that comprised of preprocessing,
feature extraction and matching. Preprocessing is
further subdivided into normalization, segmentation,
enhancement and core point detection. The feature
extraction might be based on minutiae extraction or
image based method that is Gabor filter in which feature
vectors are extracted. Moreover we have presented an
effective verification technique that employs the SVM
classifier and compares it with three distance measures.