A Novel Approach To Reduce Fingerprint
Sensors Interoperability Problem
Nitin Sambharwal1, Dr. Chander Kant2
1
Research Scholar, Department of Computer Science and Applications, K.U., Kurukshetra, INDIA
[email protected]
2
Asst. Professor, Department of Computer Science and Applications, K.U., Kurukshetra, INDIA
[email protected]
Abstract: In the recent year studies, biometrics is being used in that place where highly secure applications are
needed. This emerging technology has been successfully deployed in market but there are some problems and
negative aspect as well of using biometric system is that sensor interoperability. Most of biometric systems
functioning under the supposition that the input data is to be compared by template that is already stored
database if both the data are obtained from the same sensor then it is fine otherwise the data is obtained from
different sensors than there are maximum chances to reject the claim identity even if the input data and stored
template are of same person. This type of problem is known as sensor interoperability. Here, in this paper some
parameters like pixel density, resolution, SN ratio, motion blur, gray scale etc and proposed algorithm to
overcome this problem are also included.
Index Terms: Fingerprint, Sensor parameters, Sensor interoperability, Interoperability Algorithm.
1. INTRODUCTION
Fingerprints are basically a texture patterns that
consist of ridges and valleys that are present on the
tip of finger. Mainly there are three basic patterns of
fingerprint ridges that are arch, loop, and whorl on
the basis of these pattern we can easily differentiate
the identity and these are shown in Fig 1. with
example as well. [1]



Fig. 1 (a) Arch
(b) Loop
(c) Whorl
Arch: In this pattern the ridge that runs along
the fingertip and curves up in the middle. Where
as in tented arches they have sharp spiked
effect.
Loop: Basically in a loop they have a stronger
curve rather than arches and they enter and exist
on the same side. Whereas Radial loops slant
toward the thumb & ulnar loops loop away from
the thumb impression.
Whorl: an oval arrangement of ridge lines,
often making a spiral pattern around a central
point. Principal types are a plain whorl and a
central pocket loop whorl, double loop whorl &
finally Accidental whorl.
Advances in the sensor technology as in resulting,
now authorize the online acquisition of fingerprint
using scanners based on optical, multispectral,
capacitive, piezoelectric, thermal or ultrasonic
principles based. All these sensors having their own
parameters like resolution, grey scale, pixel density
etc are explained in sec. 1.1 [1].
1.1 Some Basic Parameter of Fingerprint
Scanner:
There are two standards for fingerprints i.e. EFTS
and PIV [2]. According to these values there are
some parameters on which fingerprint scanner
works and these are shown in Fig 2.[1] [3]:
6. Dynamic range (depth): It is defined as the no
of bits used to encode the intensity value of each
pixel. Its value is generally taken as 8.
7. No. of pixels: No of pixel is derived from the
value of resolution and acquisition area. Let
resolution is denoted by r dpi and area as height
(h)*width (w) then no. of pixels is given by
(rh*rw) pixels.
1.2 Sensor interoperability:
Fig 2: Basic Parameter of Fingerprint Scanner
1. Resolution: It is explained as pixels per inch
(ppi) or dots per inch (dpi). Maximum and
minimum allowed resolution of biometric sensor is
500dpi±10%.
2. Acquisition area or Sensing area: A rectangular
type area that senses the fingerprint and it is given
by height*width. If more the sensing area more
good quality of sensor it is because it captures more
ridges and valleys. But more sensing area poses
problem of incorporation in small devices and cost
of sensor is also increased. Generally the area for
correct capturing is 1×1 square inches.
3. Signal to noise ratio(S-N ratio): It is defined as
the ratio of signal power to noise power. Typical
S-N ratio is 500±2%.
4. Geometrical accuracy: Maximum geometric
distortion introduced by acquisition device is known
as geometrical accuracy. It is expressed in the form
of percentage with respect to x and y direction. Its
typical value is max {0.0007’’, 0.01’’}.
5. Gray scale range: Value of gray scale range
should be greater than equal to 128. It convert the
capture image in grey level.
Sensor interoperability refers to the ability of a
biometric system to adapt to the raw data obtained
from a variety of sensors. Most biometric systems
are designed to compare data originating from the
same sensor. In some cases the classifiers are
trained on data obtained using a single sensor alone
thereby restricting their ability to act on data from
other sensors. The inherent variation in the procured
images is illustrated in Fig. 3
Fig 3. Observed differences between impressions of
the same finger acquired using five different
sensors. Verifier 300, Hamster III and U.are.U 4000
are optical sensors. Hamster III is based on SEIR
(Surface
Enhanced
Irregular
Reflection)
technology, while U.are.U 4000 uses a FTIR
(Frustrated Total Internal Reflection) technology.
The USB 2500 is an electro-optical sensor and the
100AX is a capacitive sensor.
where five different scanners are used to capture
impressions of the same fingerprint. These
variations occur because of different parameter for
different sensor such as resolution, sensing area, etc.
impact the features extracted from the images (e.g.,
minutiae points) and propagate into the stored
templates. Most fingerprint matchers are restricted
in their ability to compare fingerprints originating
from two different sensors resulting in poor intersensor performance that will degrades the biometric
system performance. It is clear from the Fig. 3 that
every sensor will its own parameters to acquire
image or data from the user [4].
2. RELATED WORK
Related work points out the significance of
investigate the impact of diverse fingerprints
capture platforms on match error rates. Poh et al.
designed a Bayesian Belief Network (BBN) to
estimate the posterior probability of the device d
given quality q, referred to as p(d|q) [7] [8]. Jain
and Ross measured the interoperability issue as one
related to the changeability introduced in the feature
set when using different sensor technologies (e.g.
capacitive vs. optical) [9]. Ross and Nadgir
subsequently proposed a compensation model
which computed the relative distortion between
images acquired using different devices [10].
Campbell and Madden conducted a study to
understand the causes of the lack of interoperability
by analyzing both native (enrollment and
verification using the same device) and non-native
(enrollment and verification using different devices)
False Match (FM) and False Non-Match Rates
(FNMR) [11]. Recently, Lugini et al. analyzed the
problem from a statistic perspective in order to
measure the degree of change in match scores when
devices used for enrollment and verification are
different [12]. Modi et al. observed that optical
touch sensors typically present a better image
quality across sensors and that similarity of
minutiae counts are not related to a specific
acquisition technology or interaction type. An
interesting study was performed by Kukula et al.
who investigated the effects of force levels on
matching error rates, minutiae count and image
quality in order to assess differences between
optical and capacitive sensors [13]. Emanuela
Marasco, design and evaluate a set of characteristics
suitable for measuring differences in fingerprint
image acquisition by different sensors [14].
3. PROPOSED FRAMEWORK
Fingerprint sensors aim to obtain a good quality
image of the ridge pattern. The quality of a
fingerprint image depends on sensor characteristics
and the condition of the finger surface. In fact,
inherent characteristics of the fingerprint (e.g., the
absence of or poorly defined ridges), fingerprint
conditions (e.g., wet, dry), poor contact of the finger
with the sensor, presence of noise, latent images
(e.g., traces from the previous user), ergonomics of
the device (e.g., ease of use, alignment) and
pressure of the finger during capture are the main
factors impacting the quality [15].
This study starts by examining whether fingerprint
images captured with different devices exhibit
similarity in image quality characteristics, minutiae
count, grey-level intensity distribution, etc. after
apply some mathematical function (N) then we can
use these quality measures to assess whether a
match score between two fingerprints represents a
genuine or an impostor comparison. The
architecture of the proposed approach is described
in Fig. 4. A set of suitable features is defined and
combined with the match score created by a typical
biometric matcher. Used features are described
below.


Image Quality measures the degree of
usefulness of a biometric sample for automated
recognition. The quality of captured biometric
data directly impacts the effectiveness of the
matching process.
Minutiae Count represents the number of
minutiae extracted from an image. A minutiaebased matcher might not be accurate if only a
few minutiae points can be extracted from the

image [16]. Minutia count may vary based on
human-sensor interaction.
Alignment relates two impressions of a finger.
They may be different depending upon the
placement of the finger on the sensor. The
alignment process geometrically transforms two
sets of minutiae points to the same coordinate
system. Each minutiae is represented as a triplet
m = [x,y,_] that indicates minutiae location coordinates and angle [16] [17].
3.1 Architecture of Proposed Scheme:
In this proposed architecture when a user place its
finger over a sensor surface then it would capture
the data and then extract the features set from the
sample. After this apply mathematical function (N)
(explained in Sec. 3.2)on both the image i.e.
A= Original image stored in data base with template
A’= Captured image from user.
1. Capture fingerprint image.
2. Find out the center part of fingerprint i.e. Core.
3. Draw grid lines. ( i.e. matrix of n×m ) in 2Dimentional i.e. X & Y axis.
4. Extract the minutiae point (ridge ending,
bifurcation, etc.)
5. Try to assign minutiae point at core O (0,0)
(Zero, Zero) at center. And starting from top (Y
Positive -axis) assign every minutiae point a
number either in Clockwise direction.
6. Scan the whole image in this manner.
7. Calculate the distance from every minutiae point
to O i.e. core to center.
(O,1),(O,2),(O,3)……………………(O,N)
8. Apply the mathematical Function (N) on both
images.
After comparing if they produce same result i.e.
“Genuine” otherwise “Imposter”
(O1)2 + (O2)2
N =
X((O1)2 - (O2)2)
Where X= any prime number(2, 3, 5,7,11,13….)
9. If the ratio of (N) equal to (N’) number
then
GENUINE
Else
IMPOSTER.
3.3 Experimental Analysis
Fig. 4 Architecture of Proposed Scheme.
3.2 Proposed scheme Algorithm:
In this experiment there are 2 images of different
optical sensors one is Digital Persona U.are.U
4000(FTIR) and other is of optical Secugen
Hamster III (SEIR) image (a) & image (b) of
different
resolution
(1000*1447),(111*160)
respectively after apply mathematical function(N)
fingerprint only but later it might works on every
biometric sensor.
REFERENCES
[1]D. Maltoni, D. Maio, A. K. Jain, and S.
Prabhakar, “Handbook of Fingerprint Recognition”,
Springer, July 2009
[2] S. Prabhakar,Alexander Ivanisov, and Anil K.
Jain,”Biometric Recognition: Sensor Characterstics
and image quality”, 2008.
Fig. 5 Digital Persona U.are.U 4000(FTIR) on Left
and Secugen Hamster III (SEIR) on Right.
N =
(O1)2 + (O2)2
3((O1)2 - (O2)2)
For Image(a) O1=6.2, O2=3.1 then N would be
N= 0.9622504487
For Image(b) O1=3.2, O2= 1.6 then N
N=0.9622504486
According to this Both the values are equally almost
then we can say both the sample are of same person.
±5% in result may occur because of scale used or in
larger calculations.
4. CONCLUSION & FUTURE WORK
The need for biometric sensor interoperability is
pronounced due to the widespread deployment of
biometric systems in various applications and the
proliferation of vendors. In this paper we have
demonstrated that the problem of sensor
interoperability can be overcome by using distance
based algorithm . A significant performance
improvement is observed when the proposed
scheme is utilized to compare fingerprint images
originating from two different sensors, viz., optical
FTIR and SEIR sensors. Only a few representative
image pairs are needed for the successful
implementation of the proposed method. In future,
it will applicable on any type of sensor used in
biometric in future. Today it will only applicable on
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