1. technology and computing
2. software
3. databases

IPASJ International Journal of Computer Science (IIJCS)
Web Site: http://www.ipasj.org/IIJCS/IIJCS.htm
Email: [email protected]
ISSN 2321-5992
A Publisher for Research Motivation ........
Volume 3, Issue 3, March 2015
Centroidal Distance Based Offline Signature
Recognition Using Global and Local Features
Mr. RASKAR RAHUL BHAUSAHEB 1, Prof. SANDEEP KUMAR 2, Mr. KHARADE SACHIN 3
1
Mtech CSE Student, IES, Bhopal.
2
Asst. Professor,CSE Dept, IES, Bhopal.
3
Asst. Professor, Parikrama COE, Kashti.
ABSTRACT
Signatures continue to be an important biometric because it remains widely used as a means of personal verification and therefore
an automatic verification system is needed. In this paper we present an off-line signature verification and recognition system based
on a centroidal distance extracted such as global features and local features. A centroid is obtained from a set of genuine samples
using the features that were extracted. The centroid signature is then used as a template which is used to verify a claimed ignature.
To obtain a satisfactory measure of similarity between our template signature and the claimed signature, we use the centroidal
distance in the feature space. In our approach, we propose a novel of line signature verification scheme, the scheme is based on
selecting 21 centroid of the signature. Taking first centroid as reference point, the Euclidian distance to all 20 remaining centroid
points are calculated. Based on distance matching of centroid, classification is performed and compared them with already trained
feature points. Finally in classification stage; a simple centroidal distance measure is used as decision tool. The average
recognition accuracy obtained using this model ranges from 90% to 100% with the training set of 10 persons to 30 persons.
Keywords: Forgeries, Feature, Signature Verification, Euclidean distance, image processing, false acceptance rate, false
rejection rate, vectors.
1.INTRODUCTION
Signature verification is an important research area in the field of authentication of a person as well as documents in ecommerce and banking. The use of signatures is a common way to authenticate identity which is crucial matter in most
legal issues. It is a behavioral characteristic that is practiced over a period of time. It is not based on physical traits like
fingerprints, iris. Aside from that, it cannot be stolen unlike other means such as identification by password. However, its
verification is a different matter. Just by looking at the differences of each character in a signature is not enough to
distinguish the genuine from a forged one. Thus, signature verification system is developed .There are two methods of
signature verification. one is an on-line method to measure the sequential data such as handwriting style with a special
device. The other is an off-line method that uses an image scanner to obtain handwriting data written on paper. Online
approaches are more easy to apply than offline systems. On-line systems use special input devices such as tablets, where as
the only available information in an offline system is a static two-dimensional image obtained from scanned prewritten
signatures on a paper .The dynamic information of the pen-tip movement such as pen co-ordinates ,pressure, velocity,
acceleration ,and pen-up and pen-down can be captured by a tablet in real time but no an image scanner. The offline
method, therefore, needs to apply complex image processing techniques to segment and analyze signature shape for feature
extraction.
1.1. SIGNATURE VERIFICATION CONCEPTS
1.1.1 Types of Forgeries
 Random: These are written by those person who doesn’t know the shape of the of the original signature.
 Simple: These signatures are written by those person who knows how the signature looks like by knowing the name of
the signer without much practice.
 Skilled:These signatures written by Person who knows exactly how the original Signature looks like with proper
practice.
1.2 Types of Features
Three main types of features extracted from Signature Image which are as follows:
 Global features- These are extracted from the whole signature image. Global features
 include – Area of cropped signature , Centre of gravity, signature height to width ratio, etc.
 Local features- These are extracted from the small portion of signature image. To computate local featuresare more
expensive than global features but these features are much more accurate than global features.
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Volume 3, Issue 3, March 2015
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Email: [email protected]
ISSN 2321-5992
Local features include – local pixel density, slant features, critical points etc.
 Geometric features – It represent the characteristic geometry of a signature that keeps both their global as well as local
feature properties.
1.3 Error Rate
False Rejection Rate (FRR): This is the percentage of genuine signatures which can be rejected as false.
False Acceptance Rate (FAR): This is the percentage of forgery signatures accepted as genuine as true.
2.MAIN OBJECTIVE
The main objective of this research is to implement a new method for signature verification and recognition using centroid
method. This method scans the user signature and pre-processes it. After pre-processing the features of signature image is
extracted. Then these features verified with previously stored features so as to find out forgery. For implementing this
method we are going to implement one algorithm centroid extraction method. In centroid method we are going to find out
centroid of scanned signature then vertical and horizontal lines are drawn from that centroid. After plotting horizontal and
vertical line the image gets divided into 4 rectangles. Again this method is applied to each rectangle.Taking first centroid as
reference point,the Euclidian distance to all 20 remaining centroid points are calculated. Based on distance matching of
centroid, classification is performed and compared them with already trained feature points .several experiments are
conducted to demonstrate the ability of the proposed scheme is discriminating the genuine signatures from the forgeries.
The results of the experimentations reveal that the proposed scheme outperforms several other existing verification
methods. The objective of the work is to reduced the two vital parameters False Acceptance Rate(FAR) and False Rejection
Rate(FRR) normally used in any signature verification scheme.
3. PROPOSED METHODOLOGY
The overall architecture of our signature recognition system is shown in figure. General processing stages of the proposed
recognition systems are: Image acquisition, Preprocessing, feature extraction and classification.
3.1 Image acquisition:
Offline signatures are the signatures made on the papers. This requires specifying the resolution, image type and format to
be used in scanning image. The sheet on which signature is made is provided to scanner which gives scanned image of
signature. In this system, we are going to use scanner with suitable resolution and format. The image is acquired, by taking
signatures from 30 members. Each member is given a white sheet with 16 rectangular boxes of 3X5cm to give their
signatures. Later these sheets are scanned and segmented to get individual signature image and stored in database.
3.2 Preprocessing
After extracting the signature ,the next step is to process the extracted signature image suitable to extract features from it.
The raw data is subjected to a number of preliminary processing steps to make it usable in the descriptive stages of character
analysis. Pre-processing aims to produce data that are easy for the OCR systems to operate accurately.
The main objectives of pre-processing are:
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Volume 3, Issue 3, March 2015
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ISSN 2321-5992
 Binarization
 Noise reduction
 Stroke width normalization
 Skew correction
 Slant Removal
 Grayscale conversion
 Thinning
 Fitting to the box.
3.2.1 Binarization
Document image binarization (thresholding) refers to the conversion of a gray-scale image into a binary image. Two
categories of thresholding occurs. One is Global, picks one threshold value for the entire document image which is often
based on an estimation of the background level from the intensity histogram of the image. And other is Adaptive (local),
uses different values for each pixel according to the local area information.
3.2.2 Noise Reduction – Normalization
Noise reduction improves the quality of the document. Two main approaches
1. Filtering (masks)
2. Morphological Operations (erosion, dilation, etc)
Normalization provides a tremendous reduction in data size, thinning extracts the shape information of the characters.
3.2.3 Skew Correction
Skew Correction methods are used to align the paper document with the coordinate system of the scanner. Main approaches
for skew detection include correlation, projection profiles, Hough transform.
3.2.4 Slant Removal
The slant of handwritten texts varies from user to user. Slant removal methods are used to normalize the all characters to a
standard form. The Popular deslanting techniques are Bozinovic – Shrihari Method (BSM) and Calculation of the average
angle of near-vertical elements
Multilevel Authentication System F
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A Publisher for Research Motivation ........
Volume 3, Issue 3, March 2015
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Email: [email protected]
ISSN 2321-5992
3.2.5 Grayscale Conversion: Since the scanned images are stored in database as a color image, a three dimensional image
(MXNX3) is not suitable for further processing, and should be converted into a grayscale image where each pixel is
represented by a value in the range 0 to 255.
3.2.6 Thinning: To eliminate the strokes in signature, thinning morphological operator is applied.
3.2.7 Fitting Boundary Box: Individual signature images are automatically cropped to the size of the signature using
fitting rectangular boundary box algorithm described in Algorithm 2.1, so that unnecessary areas are removed. It allows
reducing the total number of pixels in the analyzed image (Fig. 2).
Algorithm : Fitting boundary box
Input: 8-bit binary image.
Output: Signature image reduced in size
Start
Step 1: Read signature image from specified location.
Step 2: Find the size of an image (row, column).
Step 3: Scan the image from top row, For (I=1 to row) do the following
 find the sum of Ith row pixels
 if sum is less than columns
 save that row number in k1 and stop scanning.
Step 4: Scan the image from the bottom row,
 For (I=row to 1) do the following
 find the sum of Ith row pixels.
 if sum is less than column
 save that row number in k2 and stop scanning.
Step 5: Scan the image from right most column,
 for (I=1 to column) do the following
 find the sum of Ith column pixels
 if sum is less than row
 save that column number in k3 and stop scanning.
Step 6: Scan the image from left most column,
 for (I=column to 1) do the following
 find the sum of Ith column pixels
 if sum is less than row
 save that column number in k4 and stop scanning.
Step 7: Store the pixels of original image from row k1 to k2 and from column k3 to k4 in the variable I2.
Step 8: Take I2 as a signature image reduced in size
Stop
3.3 Feature Extraction
This is the most important stage of any offline signature verification system. This stage which makes each offline signature
system different from other offline system because signature acquisition and preprocessing is somewhat common stages in
all systems but the features extracted different from each system which makes every offline signature verification system
unique features available to extract in offline signature. In this System, the features extracted are Signature centroids.
These extracted features from the basis to compare and there by classify signature either genuine or forge.
In this step, suitable global and local features are extracted from the image. The procedure employed in this stage is
described in the following steps. First- the global features such as height, width and area are extracted from whole image.
Second- DWT (Discrete Wavelet Transform) is applied on signature image and maximum vertical projection position and
maximum horizontal projection position features are extracted.
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IPASJ International Journal of Computer Science (IIJCS)
A Publisher for Research Motivation ........
Volume 3, Issue 3, March 2015
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Email: [email protected]
ISSN 2321-5992
3.5.Training: This involves developing a Euclidean Distance and Knowledge base.
3.6. Testing : In testing, all the features are extracted from the testing image. These features are given to the Euclidean
Distance, which classifies given sample and produces output to identify the owner of a signature. The subsequent section of
the paper describes the features employed, classifier used and the experimentation conducted
4.FEATURED EMPLOYED
To recognize the signature images, global features mainly height, width, Centroid-x, Centroid-y, area, maximum vertical
projection position and maximum horizontal projection position and powerful wavelet energy features are employed.
Height, Width, Area and Centroid-x, Centroid-y features are shown in the fig. 3. Centroid-x and Centroid-y indicate point
with highest pexel density. The row, which has highest signature pixels, is taken as maximum horizontal projection
position. The column, which has highest signature pixels, is taken as maximum vertical projection position. These two
features are extracted from the sub images obtained w. The Energy features are extracted using technique called wavelet.
Mainly one level Discrete Wavelet Transform is applied on each block of the signature image to decompose it into four subimages.
One of these sub-images is a smoothed version (approximation co-efficients) of the original image corresponding to the low
pass information and the other three ones are high pass information (detailed co-efficients) that represent the horizontal,
vertical and diagonal edges of the image respectively. The procedure followed involves following steps. First: The whole
image is divided into 16 blocks as shown in the fig. 4. Second: First level DWT with the filter called ‘db4’ is applied to each
of the 16 blocks. DWT decomposes each block into four sub-images as shown in the fig.5. Third: For each of the detailed
coefficient matrices, energy features are extracted using the equation (1) , Where MXN - Size of the sub image, I(i,j) - Total
Number of White Pixels
All the 20 features extracted by the above procedure is listed in the TABLE 1.
5.CLASSIFICATION USING CENTROIDAL DISTANCE
This is last stage of any offline signature verification system in which the features extracted from database are compared
with the features from test signatures and based on the classification criteria the signature are classified either genuine or
forged .In this system classification is based on angle pixel matching, Stroke speed and signature centroid distance
matching is used to classify signature.
Euclidean Distance Model is used for classification. This classifier is good for features extracted and fast in computation.
This is the simple distance between a pair of vectors of size n. We can calculate distance using Euclidean distance model by
following equation. In threshold calculation these distances are useful. Let X (x1, x2…..xn) and
Y(y1,y2….yn) are two vectors of size n. We can calculate distance (d) by using equation.
The Euclidean distance or Euclidean metric is the "ordinary" distance between two points that one would measure with a
ruler, and is given by the Pythagorean formula. By using this formula as distance, Euclidean space (or even any inner
product space) becomes a metric space. The associated norm is called the Euclidean norm.
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IPASJ International Journal of Computer Science (IIJCS)
A Publisher for Research Motivation ........
Volume 3, Issue 3, March 2015
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Email: [email protected]
ISSN 2321-5992
The Euclidean distance between points p and q is the length of the line segment connecting them. In Cartesian coordinates,
if p = (p1, p2,..., pn) and q = (q1, q2,..., qn) are two points in Euclidean n-space, then the distance from p to q, or from q to
p is given by equation 2.
Using Euclidean distance metric, similarity score between any two feature set can be obtained in terms of the extracted
features. The distance is used as the matching criterion, i.e. a signature is matched if this distance lies in a range of
subjective threshold. However, using the Euclidean distance we generate matching scores by matching a query signature
with all the signatures of database
A. Testing : In testing, all the features of test image are extracted. Using the Euclidean distance we generate matching
scores by matching a test signature features with all the signatures of database, which classifies given sample and produces
output to identify the owner of a signature. A signature is matched if this distance lies in a range of subjective threshold.
6. EXPERIMENTATION
Two separate experiments were conducted using the proposed model and results of the experiments are described in this
section.
A.Experiment 1
The first experiment has been performed on a signature database where features of each signature were extracted. The
features extracted in this experiment are, Height, Width and Area of the signature, horizontal and vertical projections and
energy values. Further, the DWT was applied to the whole image to get horizontal, vertical and diagonal components. From
each component we extracted horizontal and vertical projections. After that signature image is divided into 16 blocks.
Energy values of each block were extracted. Identification rate resulted from this experiment is shown in Fig. is 70% for 30
person database which is very less.
B. Experiment 2
In this experiment, DWT is applied on each of the 16 blocks to get 16*3 sub images and Energy features (16*3) are
extracted from these sub images. Also the two global features, Centroid-x and Centroid-y are added. The performance rate
shown in the Fig.7 has increased to 90-100% for the enrollment of 10-30 people.
The experiments conducted indicate that as the number of different signatures increases the performance accuracy is
decreased. Also with the increase of training samples the performance accuracy has been increased.
7.EXCEPTED OUTCOME
The biometrics characteristics of a sample signature can never be exactly the same as those provided during the registration
procedure for the same id. This requires the matching algorithm to return results which are near matches to the
characteristics given, and hence the need for a threshold arises. In the signature verification system an upper threshold is
used i.e. all the matches which are below this specified threshold are said to be valid and the others are rejected.
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A Publisher for Research Motivation ........
Volume 3, Issue 3, March 2015
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Email: [email protected]
ISSN 2321-5992
The performance is measured in certain standard terms these are as follow:
False Acceptance Rate (FAR):
It is the ratio of the number of unauthorized users accepted by the system to the total number of verification attempts made.
False Rejection Rate (FRR):
It is the ratio of the number of authorized users rejected by the system to the total number of verification attempts made.
The feature values are compared with the values in the range of maximum value and the minimum value and the result is
given. For Sigature sample 1 and Signature sample 2 the matching of values is more than the 70%. Hence both signatures
are accepted as genuine otherwise forgery.
8. CONCLUSION
In this work, an off-line signature recognition system designed using 3 stages namely pre-processing, feature extraction and
classification stage in order to make the right decision is presented. This signature recognition is based on 59 powerful
global and local wavelet features of different signatures and the recognizer employs Euclidian Distance. One of major
advantages of Euclidian Distance is ease of computation. Experimental evidence has shown that this method has provided
substantial improvements. 90% to100% accuracy is obtained for the 5*12 to 30*12 signatures used in database. This
accuracy can be improved still with the more number of samples of each signature used. One problem faced, is the lack of
samples needed to build a reliable signature recognition system and asses the performance. Unfortunately this system has
failed to recognize the some rotated signatures. Future avenues of this work include an analysis of new features of signature
image and combining those with the feature vectors used in this work to obtain better accuracy than the accuracy of present
work.
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AUTHOR
Raskar Rahul Bhausaheb received the B.E. in Computer Engineering from Vidya Pratishthan’s College Of Engineering,
Baramati in 2011.Now, He is doing M.Tech in Computer Science and Engineering from Rajiv Gandhi Proudyogiki
Vishwavidyalaya, Bhopal.
Prof.Sandeep Kumar received the M.Tech. in Computer Science and Engineering from RGPV, Bhopal. Now, he is
working as Assistant Professor in IES , Bhopal..
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