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International Journal of Computer Engineering and Applications, Volume VI, Issue I April.14
www.ijcea.com ISSN 2321-3469
2D Fingerprint Matching using Multi- dimensional Artificial Neural
Networks
Ms. Vishakha R. Bhadane, Dr.M.V.Deshpande, Mr. Prashant Mishra
Department of Computer Engineering,MPSTME, SVKM’s NMIMS, Shirpur, Maharashtra, India
Abstract- Biometrics is a technology which identifies a person based on physiological or behavioral
characteristics. Fingerprint identification and recognition is one of the biometric methods available that
has been widely used in variousapplications due to its reliability and accuracy in the process of
identification and authentication of a person. The main objective of this work is to develop fingerprint
identification and recognition system. The system consists of three main parts; are image acquisition,
image processing and "identification". The fingerprint images captured sensor format and stored in the
database in the process of image acquisition. This image is the commitment to share 500 DPI. These
images are then amplified in stage image processing by performing gray level enhancement, Gabor
filtering, image sharpening, edge detection, segmentation and thinning processes, then the minutia
extraction method using the cross number and alignment images. We present a hybrid algorithm that
uses both minutiae matching (point) information and texture (region), adjustment information of
fingerprints. Once the image processing could then be inserted into the back propagation neural
network as input to train the network. After training, the neural network is ready to perform the
identification and recognition function (matchingprocess). A neural network has been successfully
developed to detect and recognize the essential part of fingerprint images.
Keywords-Fingerprint Recognition, back propagation,multi-dimensional ANN
I.
INTRODUCTION
Fingerprints are the most used technique biometrics for personal identification. There are two
main applications include fingerprints: verification of fingerprints and fingerprint identification
[1]. While the purpose of the verification of fingerprints to verify a person's identity, the
purpose of fingerprint identification is to identify an individual. In the last three decades,
automatic verification of fingerprints is becoming more widely than other techniques of
biometrics, such as face recognition and signature recognition.
Biometrics is the emerging field in technology with unique and measurable "physical and
behavioral” characteristics that can be processed electronically by creating recognition , run
control and automatic face recognition . These physical properties include the appearance of the
face, fingerprints, hand geometry , handwriting , iris , retina , voice and veins . Fingerprints
have been used for over a century and is the most widely used form of biometric identification .
Fingerprint identification is mainly used in the field of artificial intelligence. It is popular
because of its easy access , low price of fingerprint sensors , non -intrusive scanning, and
relatively good performance. In recent years , there have been significant improvements in
performance were achieved in commercial automatic fingerprint identification . The footprint
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International Journal of Computer Engineering and Applications, Volume VI, Issue I April.14
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of an individual is unique and remains unchanged over a lifetime. No two people have the same
set of fingerprints. This property makes an excellent fingerprint biometric identifier. So it is one
of the popular and effective means to identify an individual and used as forensic evidence . A
fingerprint is formed from an impression of the pattern of ridges on a finger. A ridge is defined
as a single curved portion and a valley is the region between two adjacent ridges .Usually ,
there are two prominent types of minutiae (ridge ending and ridge bifurcation ) , forming a
fingerprint . The minutiae are local discontinuities in the flow pattern ridge provides the
features used for the recognition . The details such as the type , orientation and location of the
minutiae taken into account when performing minutiae extraction [1 ] . Here , we propose a
fingerprint matching system based on artificial neural network (ANN ) . The rest of the paper is
organized as follows : Section II provides a brief description of a general fingerprint
recognition system . Section III provides the background principles associated with the
operation of the proposed model . All the experimental results and related discussion is
provided in Section IV - V. This paper concluded by summing up the work in Chapter VI.Some
of the relevant literatures cited from [21] [18].
II.
ARCHITECTURE OF FINGERPRINT MATCHING SYSETM
Fingerprint Identification System based can operate either identification or verification
operation. Fingerprint identification refers to one-to - many matches where fingerprint input
image of a person associated with any standards that exist in the database. Confirms the identity
of a person. Fingerprint Identification System follows four basic steps is Image Acquisition
/Registration, image enhancement preprocessing, feature extraction and Matching.
Fig. 1. General block diagram of Fingerprint recognitionSystem
Figure understands the automated fingerprint authentication comprising the following phases
[5]:
a) Fingerprint Acquisition / Register
b) Fingerprint Image Enhancement
c) Extraction of Minutiae
d) Minutiae Matching
e) fingerprint classification / Authentication
A. Image Acquisitions
At this stage, the image of the fingerprint was arrested for the first time with the help of
sensors. Acquired images can be blurred or may contain noise, which lowers image quality
directly affects the rate of return of fingerprint recognition system. The fingerprint image
acquired can vary the position, direction and stretching degree [5].
B. Pre-processing
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Ms. Vishakha R. Bhadane, Dr.M.V.Deshpande, Mr. Prashant Mishra
International Journal of Computer Engineering and Applications, Volume VI, Issue I April.14
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After capturing an image from the image or image enhancement preprocessing applied to the
image. Sometimes it can contain image noise while recording process; noise can be removed
using filters in treatment / improvement phase. Images must be normalized; this can also be
done in the pretreatment step. You must have completed this phase.
C. FeatureExtraction
After the process of feature extraction, preprocessing is performed. The phase characteristics of
image export function exported as Ridges, valleys and minutiae, singular points (loops, core,
coils and delta), etc. These features are useful for identification or verification of individuals.
The features extracted from the captured images stored in the database for further process of
matching.
D.Matching
Next step is the connection process after feature extraction. Phase matching identifies
similarities between the mainpattern recognition and pattern. Different approaches pairing. The
input images provided in the system associated with the primary standards that exist in the
database . Matching is completely dependent on whether the systemperforms identification or
verification. While recognizing that performs one - used to-many matching approach ,where the
fingerprints of a person fits all the available templates under various one-to - one fight is to
control where the input image of a person associated only with a template person who claims to
be.
III FINGERPRINT CLASSIFICATION
Fingerprint classification defines the global standard representations of fingerprints. Worldwide
representations include positions of the points (e.g. , the nucleus , and d) a fingerprint. A
standardclassification of fingerprints are categorized in the following six categories: whorl,
right loop, left loop,arch , twin loop, arch and scenes . Also contains one or more regions where
the lines consume different ridge shapes (curvature, termination, etc.). These areas (called
singular anomalies or areas) can be classified into three typologies: loop, delta, and whorl[7]
Fig. 2. Structure of fingerprint. (a) Ridges and valleys on afingerprint image; b) singular regions (white boxes)
and corepoints (small circles) in fingerprint images [4]
I.
SURVEY OF FINGERPRINT MATCHING TECHNIQUES USING
ARTIFICIAL NEURAL NETWORKS
Different classification techniques have been proposed by various authors in the field of
Artificial Neural Networks.ANN used to give greater accuracy and learning pace. It is basically
used for classification purposes in image processing. Fingerprints are traditionally classified
into categories based on the information in global standards ridges. The classification of
fingerprint into groups reduces the need to match an input fingerprint with the entire fingerprint
database during the process of detection and recognition, and therefore to reduce computational
requirements. Two classifiers,ie , the mean and 3 K - nearest neighbor , used to extract the
classifier has four different types of fingerprints, ie Arch, Left Loop, Loop right, or spiral . The
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International Journal of Computer Engineering and Applications, Volume VI, Issue I April.14
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method achievehigh classification accuracy and quick to produce results . Tan, X. ,Bhanu, B. ,
Lin, Y, used a typical algorithm - learning using genetic programming (GP) to learn and
capabilities have evolved image processing operators to classify fingerprints. The primitive
operators used were simple and easy to calculate . These bodies were divided into vectors and
calculating feature vectors generation. This classification method may be found to be effective
with the quality of fingerprint images [10] .
Maheswari and Chandra [22], shows the classification scheme of fingerprints operation Fuzzy
artificial neural network. The characteristics of the fingerprint, such as singular points, the
positions and orientations of the core and delta points from a binary fingerprint image captured
by the sensors. The method used to produce the good results of classification using fuzzy neural
networks. An algorithm used for two machine learning algorithms presented by [15]. They used
Support Vector Machine (SVM) and Recursive Neural Networks (RNNs) during classification.
The RNNs are trained on a structured representation of the fingerprint images and also used to
extract a set of distributed features incorporated in SVMs. SVMs are combined with error
correction coding scheme , which exploits the information contained in ambiguous fingerprint
images .
Kant and Nath presented an approach that improves the speed, efficiency of fingerprint
matching algorithm during the same record. For this reason , the hard points of fingerprints, as
delta and core classifiers used and grouped into any of the other six categories. [20] According
to the Wei L, anomaly detection can be used to increase the accuracy of the classification
algorithms and proposed a method for searching mannerisms use index delta field Poincare.
Used these abnormalities , a quick rule -based classification algorithm was proposed to classify
fingerprints into five categories, arch, tents , left loop, right loop , whorl and double loop . The
detection algorithm searches the direction field which has the larger direction changes to get the
defects [16]. Wei, Yonghui and Fang [18] proposed an approach based structure based on
characteristics curve ridgelines, used for classification of fingerprint with other fingerprint
images available in the database. The algorithm mainly uses the direction for the classification
of fingerprint ridgelines. In this method, the classifier is first calculated the total change of
direction ridgelines is grouped according to their shape. Grouped ridgelines along with the
extracted singular points used for classification of fingerprint arch, tents, left loop, right loop ,
whorl and double loop courses . Combining singular points and orientation image information
for fingerprint classification proposed by [19]. Algorithm says those singular points and limit
non -linear orientation and the final feature vector consisting of the coefficients of the model
orientation and uniqueness information. This led to a compact feature vector used as input to a
Support Vector Machine (SVM) to perform image classification.Chandra Maheswari and has
used low dimensional features obtained from the feedback -based line detector to classify
fingerprints into five classes ( arch , left loop , right loop , whorl , arch and scenes ) . The
detector line was a cooperative dynamic system that gives oriented lines and maintain multiple
directions at points where different oriented lines meet . The feature extraction based on
characterizing the distribution of orientations around the fingerprint. Three types of classifiers
used namely , support vector machines , nearest neighbor , and neural network [14]. An
algorithm used for two machine learning algorithms presented by [5]. They used Support
Vector Machine (SVM) and Recursive Neural Networks (RNNs) during classification . The
RNNs are trained on a structured representation of the fingerprint images and also used to
extract a set of distributed features incorporated in SVMs. SVMs are combined with error
correction coding scheme , which exploits the information contained in ambiguous fingerprint
images .
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Ms. Vishakha R. Bhadane, Dr.M.V.Deshpande, Mr. Prashant Mishra
International Journal of Computer Engineering and Applications, Volume VI, Issue I April.14
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II. PROPOSED WORK
The above discussed methods have some limitations which have solved by many researchers,
but there are still some other issues in fingerprint recognition and matching systems like
computational time and efficiency of recognizing the faces from large databases.
Figure 3: Overall system description diagram
Figure understands the automated fingerprint authentication comprising the following Modules:
1] Data Capturing Module
2] Image Enhancement Module
3] Minutia Extraction
4] Recognition and Matching Using MDANN
5] Decision Making Module
1. Data Capturing:
Data logging is offenly, as it is called, also, obtaining fingerprints. Altitude fingerprints can be
either offline (inked) or Online (Live scan). In a method inked fingerprint of a finger taken
initially impregnated in a paper, which is then scanned. This method usually produces images
of very poor quality due to the non- uniform dispersion of ink and therefore not practiced online
touch [11]. In this online fingerprinting system sensor is a capacitive or optical fingerprint
scanner which is more expensive, patented fingerprint sensor with USB Optical resolution
500DPI. The image size is captured form the fingerprint reader is 260x300 pixels. The most
important part of the system is the living image of the fingerprint capture section which is also
called as smart capture that capture high quality fingerprints from dry , wet , scarred and age is
difficult to detect fingers more accurately and reliably. After taking Pictures of the fingerprint
reader will be given to that section image enhancement.
2. Fingerprint Image Enhancement / Preprocessing
A critical step in automatic fingerprint matching is automatically and reliably extracts minutiae
from the input fingerprint images. However, the performance of a minutiae extraction relies
heavily on the quality of input fingerprint images. To ensure that the performance of an
automatic fingerprint matching system is strong in relation to the quality of the fingerprint
images, it is essential to incorporate a fingerprint enhancement
A. Segmentation
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International Journal of Computer Engineering and Applications, Volume VI, Issue I April.14
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Segmentation is the process of telling the background area of the foreground regions. It is
observed that the background areas are low gray scale variation compared to the foreground
region. Thus, a method based on the threshold variation is used for the partition [24].
V(k) =
where V(k) isthevarianceand
isthepixelvalueatthepixel(i,j).
M(k)
(1)
isthemeangray-levelvalue
of
theblock
k.
I(i,j)
B. Normalization
Normalization is done in order to standardize the dynamic variation in the gray-level values.
N(I,j) =
if I(I,j)>M (2)
Otherwise
(3)
where ‘N(i,j)’ is the normalized gray-level value and I(i,j) is the gray-level value at pixel(i,j).
‘M’ and ‘V ‘are the estimated mean and variance of I(i,j) respectively and ‘ ’ and ‘ ’ are the
desired mean and variance values respectively.Basically the best normalization technique now a
day's is mostly used is histogram equalization. Histogram Normalization is one of the most
commonly used methods for preprocessing. In image processing, the idea of equalizing a
histogram is to stretch and redistribute the original histogram using the entire range of discrete
levels of the image, in a way that an enhancement of image contrast is achieved. The most
commonly used histogram normalization technique is histogram equalization where one
attempts to change the image histogram into a histogram that is constant for all brightness
values. This would correspond to a brightness distribution where all values are equally
probable. For image I (x, y) with discrete k gray values histogram is defined by ie the
probability of occurrence of the gray level i is given by:
P (i) =
(4)
Where i∈ 0, 1…k −1 grey level and N is total number of pixels in the image. Transformation to
a new intensity value is defined by:
=
=
(5)
Output values are from domain of [0, 1].To obtain pixel values in to original domain, it must be
rescaled by the K−1 value.
C. Filtration
Along with the field orientation, the ridge frequency is also required for the construction of the
filter Gabor. This represents the frequency of the local ridge of the fingerprint. Gabor filters
optimally capture both local orientation and frequency information from the image of the
fingerprint. By tuning a Gabor filter with a certain frequency and direction, can be obtained by
the local frequency information and orientation [7] [8]. It is suitable for extracting information
from images texture [9].
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International Journal of Computer Engineering and Applications, Volume VI, Issue I April.14
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Another symmetric Gabor filter has the following general form in the spatial domain [10]:
G(x, y; θ, f) = exp
(6)
x’ = x sinθ+ y cosθ
(7)
y’ = x cosθ- y sin θ
(8)
wheref is the frequency of the sinusoidal plane wave along the direction θ from the x-axis, and x
δ and y δ are the space constants of the Gaussian envelope along x and y axes, respectively.
D. Binarization
Binarization is to convert the grayscale image to a binary image that is either zero or
oneasthepixelintensity value using a certain threshold.
E. Thinning
This is the final stage of image enhancement. This is a process which erodes morphological
image successively until a skeletal image of the fingerprint which is one pixel wide isobtained.
This image is in turn used for the extraction process of the minutiae [24].
3 Minutia Extractions
The enhanced image is used to extract the minutiae points of the fingerprint. There are a
number of features that could be used for identification of fingerprints, but mostly minutiae
points is limited to two types namely the ending ridge and the ridge bifurcation. Ridge endings
is the point where the curve ends ridge and ridge bifurcations are the points where the curve
ridge splits from a single path to two in a Y - junction. The minutia points extracted from the
enhanced fingerprint image by using the concept of Crossing Number (CN). The value CN for a
ridge pixel P is given by the equation [24].
CN =
(9)
The value of the number of codes used to distinguish between types of minutiae. The minutiae
points determined by scanning the neighborhood of each pixel in the ridge thinned image using
a 3 × 3 window (Figure 4) [14].
Fig. 4. a) Ridge ending and b) bifurcation in c) 3×3 window [14].
A. Image Alignment:
Minutiae points of both the input and template images are extracted using the algorithm
described in [6]. The algorithm provides the following two outputs: (a) A set of minutiae points,
each characterized by spatial position and orientation of the image of the fingerprint. (b) Local
Information ridge in the vicinity of each point in detail. The two sets of minutiae points then
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combined using an algorithm to match point. The algorithm first selects a reference minutiae
pair (one from each image), and then determines minutiae pairs using points. [4].
Fig. 5. Masking out background regions: (a) Template image, (b) Input image, (c) Input image after
translation and rotation, (d) Masked input image[4].
An exhaustive evaluation of all point correspondences avoided because of the availability of
local ridge information at each point in detail. Once the minutiae points are aligned with this
method, the rotation and translation parameters calculated. Estimated rotation parameter is the
average of individual rotation values of all corresponding pairs of minutiae. The parameters of
translation are calculated using the spatial coordinates of the reference minutiae pair resulted in
better alignment [4].
4.Recognition and Matching Using MDANN
Next phase is matching process after feature extraction. Feature matching phase identifies
similarities between recognition templates and master template. The new matching approach is
used called as hybrid matching approach.
A. The Hybrid Matching Approach
The matching technique described herein is referred to as “hybrid" technique because it
combines information available minutiae in a fingerprint with the underlying texture
information in local areas to perform the match. Combining an input image with a stored
template includes the calculation of the sum of squares of differences between the two features
vectors after discarding missing values .This distance is normalized by the number of valid
values feature used to calculate thedistance. The score matching combined with that obtained
by the method based on minutiae, using the sum rule of combination. If the matching score is
less than a predetermined threshold, the input image is said to have successfully combined with
standard [4].
Everyone is known to have unique, immutable fingerprints. A fingerprint is made of a series of
ridges and grooves on the surface of the finger. Minutiae points are local ridge characteristics
that occur at either a ridge bifurcation or a ridge ending. Fingerprint matching techniques can
be placed into two categories: minutiae - based and correlation based. Technical details based
first minutiae points and then map their relative placement on the finger. However, there are
some difficulties in using this approach. It is difficult to extract the minutiae points accurately
when the fingerprint is of low quality. The method is based correlation is able to overcome
some of the difficulties of the minutiae based approach. However, this has some own
shortcomings. Correlation based techniques require the precise location of a registration point
and are affected by image translation and rotation [22].
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International Journal of Computer Engineering and Applications, Volume VI, Issue I April.14
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Fig. 6.1. Minutiae points in a Fingerprint image[22].
Fig. 6.2. Matching the finger print images [22].
Fingerprint matching minutiae has problems in matching different sized (unregistered) minutiae
patterns . Local ridge structures can not be completely characterized by minutiae . Here an
alternate representation of fingerprints tried , which will capture more information about the
area and give a fixed length code for the fingerprint. The matching will then hopefully become
a relatively simple procedure for calculating the Euclidean distance will between the two codes
. A commercial fingerprint recognition system based requires a very low False Reject Rate
(FAR) for a given False Accept Rate (FAR). This is very difficult to achieve with any one
technique . Until the investigation is underway to find methods to gather data from various
matching techniques to increase the overall accuracy of the system . In a real application , the
sensor , the acquisition system and the change in system performance over time is very critical.
A method of testing has been done on the system in a limited number of users to evaluate the
performance of the system during a period [22].
5. Recognition by MDANN
In neural networks, learning is easy , because it depends only on locally available information ,
but since the information is mixed in data storage , clear recovery of stored information is not
simple. Recognition requires knowing whether or not a new entry has been submitted before
and stored in memory. The glide slope of a generalized non- linear, multi-layer feed network
called front backpropogation. This is a supervised learning algorithm that learns by first
calculating an error signal and then transmits the error backwards through the network
assuming the weights of the network is the same in both backward and forward directions.
Backpropogation is a very popular and widely used learning algorithm of the network[24].
In this paper, we explain our work to educate pictures details on the ANN using the algorithm
backpropogation and then match the fingerprints for the identification of the database . The
MDANN used herein consists of 20 neurons, and is designed to receive the inputs of the matrix.
Figure shows the network structure of MDANN but the connections between the input and the
hidden layer is not shown in full for the purpose of clarity and complete structure is obtained by
extending connections to the entire image. The inherent advantage of using a multidimensional
ANN in this system is that the entire matrix containing the minutiae points can be used to train
the network which means that the relative orientation of minutiae points are automatically used
in recognition [24].
Fig. 7. Structure ofMDANN [24].
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Algorithms for MDANN
1. Choose a pair of education from training set; apply the input vector to the network input.
2. Calculate the output of the network.
3. Calculate the error between the network output and the desired output (target vector of the
pair training)
4. Configure the network weights in a way that minimizes the error.
5. Repeat steps 1-4 for each vector in the training set until the error for the entire set is
acceptably low.
During this stage of training the network with the fingerprint images , each fingerprint image is
assigned a unique arbitrary value . Now, different images of the same fingerprint trained the
network with the same arbitrary value as price target. The iteration continues until the value
calculated in step 4 is the target value assigned to that image. This value is unique for each
individual fingerprint image when a random fingerprint has been tested on the network , this
value ends up as to whether the image is already present in the database or not and if there is
the exact image that corresponds to .
III.
EXPERIMENTAL RESULT
First, the fingerprint image was captured using the reader SecuGen Hamster Plus. This image
was taken at a resolution of 500 DPI and the image size of 260x 300pixels. This image was 8
bit (256 levels )grayscale image . Then, the image was first enhanced in order to allow a proper
export points of detail . The image captured by the fingerprint reader to be stored directly in the
database. The actual image was first enhanced in order to allow a proper export points of detail
. The loaded image is then sent to the process of improving the image. The first step for
pretreatment comprises normalization technique wherein the histogram equalization is applied
to the image. The normalized image where dynamic stresses standardized so that prices are in a
certain range is shown in Fig. 8 (a). Fig. 8 (b) shows the binary image has a better clarity and
continuity of ridge. This binary image has only two levels of intensity values namely zero and
one which makes a clear distinction between the ridges and valleys. The abscissa image
separating the background from the foreground area shown in Fig. 8 (c).Nextly in Fig. 8 (d ) the
flow orientation is estimated that the direction of the flutes is the brand for the future extraction
of minutiae . A region of interest (ROI) is a portion of an image that we want to filter or used
for future operation of mining show in Fig. 8(e). The thin image obtained from the binary
enhanced image shown in Fig. 8(f) an abandoned for calculating prices of code and then to
extract the minutiae points. The minutiae points obtained after the certification of these points
taken from the CN method is shown in Fig .8(g).
This image is trained in multidimensional neural network using the training process
gave the following yield curve shown in Fig. 9, where the yield is defined as the squared
difference between the target price and the actual price obtained during the simulation. The
yield curve is a function of the number of repetitions. It has been observed that the performance
has improved steadily and leads to a value close to zero.
The simulations were performed in MATLAB 7.10.0 (R2010a) version with 1GB
RAM, processor Intel Core i3. The trained network was able to give the exact value every time
a picture was one of those who had been trained previously given as input. The results were
quick and were observed to have good identification captured fingerprint images.
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(a)
(b)
(c)
(d)
(e)
(f)
(g)
(a) Histogram Equalization, (b) Binary Image.
(c) Segmentation, (d) Flow Direction, (e) ROI,
(f) Thining, (f) Minutiae Extraction
Fig. 9. Performance vs. number of iterations
IV.CONCLUSION
The technique of fingerprint matching using minutiae points for identifying fingerprints have
been presented in this paper. Preprocessing techniques applied first to enhance the image and
get a thinner binary fingerprint ridge structure that can be used for minutiae extraction. The
minutiae are extracted fingerprint images from the database then trained in a multidisciplinary
artificial neural network, which takes in two-dimensional array minutiae image as an input and
output target. The trained network may then be used for the purpose of matching fingerprints in
the database . The method is based on the representation of pattern in two dimensions and
therefore maintains the details of the plan of detail in an image in the best possible way . This
method was found to give results that are comparable in terms of efficiency of recognition by
other pattern recognition techniques , and therefore , the results are promising to work to
improve MDANN for better opportunities to match. This technique neural network was also to
support fast matching fingerprints . In addition , further work in this area could be to optimize
the trade-off between speed of recognition and the size of the database that can be supported by
the network .
ACKNOWLEDGEMENT
This paper is part of the project initiatives for identification of problem definition. Author
thanks to Dr. M. V. Deshpande, Associate Dean, Mukesh Patel School of Technology
Management and Engineering, NMIMS, Shirpur and Mr.Prashant Mishra for their valuable
guidance in performing literature survey.
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Ms. Vishakha R. Bhadane, Dr.M.V.Deshpande, Mr. Prashant Mishra