This document presents research on offline signature verification using local keypoint features. It discusses existing challenges in offline signature verification like different signature orientations and image noise. The objective is to develop a robust method for offline signature verification that can handle noise, orientation variations, and different writing styles. The proposed method uses Harris corner detection to extract keypoints, and creates a 128-bin feature descriptor for each keypoint. Keypoint matching and classification using KNN is then used to verify signatures. Future work includes implementing the proposed method and improving its robustness to rotations, noise, and ink variations with minimal complexity.

1. Islamic University of Technology(IUT)
Department of Computer Science and Engineering(CSE)
Offline Signature Verification Using
Local Keypoint Features
Supervised By:
Dr. Hasanul kabir
Assistant professor, CSE dept.
Islamic university of technology(iut)
Presented By:
Ashikur Rahman (104401)
Golam Mostaeen (104404)

2. Contents
 Introduction
 Offline Signature Verification
 Research Challenges
 Thesis Objective
 Related Works
 Proposed Method
 Dataset & Implementation
 Future Works
 References
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3. INTRODUCTION
A signature is a person's name written in a distinctive way as a form of identification in
authorizing a check or document or concluding a letter.
Signature forgery refers to the act of falsely replicating the signature of another person.
Signature forgery is done in order to-
 Commit frauds
 Deceive others
 Alter data etc.
One common example of signature forgery is cheque writing.
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4. Offline Signature Verification
Those forgeries can be verified in two methods-
 Online Signature Verification
• Deals with dynamic features like
speed, pen pressure, directions,
stoke length and when the pen is
lifted from the paper
 Offline Signature Verification
• Uses features(static information)
from the image.
• Deals with shape only.
• Largely used for verifying bank
cheques
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5. Research Challenges
 Differentiating different parts of signature
that varies with each signing-
 Signature orientation can be different-
 Input image may contain noise.
 Isolating the sector of interest from the
total input image-
Threshold value should be taken wisely so
that False accept and False Reject occur very
less.
 The nature and variety of the writing
pen
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6. Thesis Objective
Our main objective of this thesis is to
develop a method that will calculate
features of a signature and verify it
comparing with sample prototype in
spite of having-
 Noise in the image
 Different orientation
 Various writing
Already we have implemented several
existing detection methods signature
verification and figured out the
limitations of the methods. Our goal is to
ensure better performance in robust
nature so that we can easily detect the
forged signature in different challenging
situations.
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7. Overall Workflow of Signature Verification
All the methods of signature verification undergoes the following steps:
 Feature is extracted (Varies from methods to methods)
 Features are classified
 The system is trained
 Matching
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8. Related Works
Existing methods so far we studied can be categorized by the following tree-
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9. Related works(contd.)
Global & geometric Method(A. C.
Verma,D. Saha,H. Saikia,2013):
• Geometric data(aspect ratio, center of
gravity, baseline shift etc.) are
considered as feature
• Mean of each feature calculated from
the training data
• Variance is used to calculate the
Euclidian distance which is the basis
of comparison
The formula is-
Limitations:
• Cannot detect skilled forgery as the
geometric value get closer
• False accept occurs more often
Angular based model(Prashanth & Raja,
2012):
• Calculate average no. of rows and
columns for random forgery detection
• For skilled, split the image in two blocks
recursively until 128 blocks have found
on basis of geometric center
• Angle and distance for all the center
points of each block is calculated from
the point (1,1) for feature extraction.
Limitation:
• Depends on global value(angle and
distance)
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10. Related works(contd.)
Grid Model(Madasu & Brian,2002):
• Image is partitioned into 8 partitions using
equal horizontal density approximation
method.
• Each Box portioned into 12 boxes (total 96
boxes)
• calculate the summation of the angles of
all points in each box taken with respect
to the bottom left corner and normalize
it for feature.
Limitations:
• Even a little change in the signature leads
to much change in the result.
Radon transform model(Kiani &
Pourreza,2011):
• Computes projection sum of the
image intensity along a radial line
oriented at a specific angle with the
formula-
Where the δ(r) is Dirac function.
• Computation of Radon Transform is its
projections across the image at arbitrary
orientations θ and offsets ρ which is
used as feature
Limitations:
• False reject rate is little high for this
method
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11. Related works(contd.)
SURF model(Bay & Gool,2006):
• First, key point is detected using fast
Hessian Detector and Haar wavelet.
The formula of Hessian matrix is-
• Then SURF descriptor is extracted using
assignment orientation.
Limitations:
• The time needed to detect can be
beaten today
G-SURF model(Pal, Chanda &
Franke,2012):
• Uses Gabor filter along with SURF
algorithm
• A two dimensional Gabor Filter in
spatial domain can be defined as
follows-
Limitations:
• Though it overcomes the performance
of SIFT and SURF, still need to be
upgraded
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12. Related works(contd.)
Harris Corner Detection:
• This method detects corner first
recognizing the points by looking the
intensity within the small window
• Shifting the window in any direction
should yield a large change in
appearance
• Different output can be gained by
setting a desirable threshold value
Feature descriptor:
• Describe a point assigning orientation
around it
• Divides the surrounding area into 16
blocks, each blocks have a histogram
of 8 bins each
• So, each point has a (16x8) 128 long
feature vector
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13. Proposed Method
 Preprocessing
• Noise removal
• Isolating area of interest
• Assignment orientation for rotation invariance
 Keypoint detection using Harris Corner Detection
 Creating Feature descriptor
• Creating 16 blocks around the keypoint
• Calculate gradient magnitude and direction
• Weigh the magnitude with Gaussian filter
• Create 8 bin histogram
• Each point have 128(16x8) bin
 Classify the descriptors using KNN classifier
 Compare the prototype with the testing signature
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14. Proposed Method(Contd.)
• Further checking for skilled Forgery
A high level of skilled forgery may pass the above
test but those can be further detected using the
following tests:
 Edge thickness will be calculated to detect
overwriting
 Straightness of the edges will be checked
 Sudden blobs in the signature need to be
detected
 End point will be checked to detect sharp
finish
If a Signature Passes all those tests we consider it
as a authentic signature.
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15. Dataset & Implementation
 Dataset
 We have collected our dataset from different persons. Signature s was
taken in white paper and scanned for training our system.
 Similarly we took forged and genuine version of the signatures for
testing the performance of different verification method we
implemented.
Global & Geometric Method:
After implementing this
method we compared its
performance for varying
threshold of acceptance. The
graph at the right represents
its performance.
 As the threshold is
increased FAR increases but
FRR is decreases somewhat
proportionately.
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16. Implementation(Cont.)
Implementation of the proposed method
For implementation of the proposed method we followed the following steps:
1. Pre-processing:
The pre-processing involves different steps. We performed the following steps
in sequences: Cropping the area of interest, noise removal and binarisation.
2. Key point extraction:
We used Harris Corner detection to find out the key point of the supplied
signature. The following right image shows the signature after key point has been
extracted from supplied left signature.
3. Feature descriptor
For each keypoint a feature vector of length 128 has been calculated. This
vector contain the histogram of orientation around the point.
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17. Future Works
• So far we have figured out several problems of existing methods of signature
detection through implementation
• Still we did not implemented our proposed method but from the implementation
& analysis of existing method we can say that it will give us better performance
• So our future work is to implement the proposed method so that it can ensure-
 more robust with rotation invariance
 robustness in noise
 robust in variant ink
with minimum complexity
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18. Thank you.
Any Question ?
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19. References
• A. C. Verma,D. Saha, H. Saikia; ’FORGERY DETECTION IN OFFLINE HANDWRITTENSIGNATURE
USING GLOBAL AND GEOMETRIC FEATURES’, IJCER(Vol.2, Issue 2, April 2013)
• Prashanth C R, K B Raja, Venugopal K R, L M Patnaik,’ Intra-modal Score level Fusion for Off-
line Signature Verification’, IJITEE, ISSN: 2278-3075, Vol.1, Issue 2, July 2012
• Prashanth C. R. and K. B. Raja,’ Off-line Signature Verification Based on Angular Features’
IJMO, Vol. 2, No. 4, August 2012
• M.Radmehr, S.M.Anisheh, I.Yousefian,’ Offline Signature Recognition using Radon Transform’,
WASET, Vol:6 2012-02-28
• Bay H,Tinne t.,Gool l.,’ SURF: Speeded Up Robust Features’;
• Samaneh G., Mohsen E., i Moghaddam, “Off-line Persian Signature Identification and
Verification Based on Image Registration and Fusion,” Journal of Multimedia, Vol. 4, No.
3, pp.137-144, June 2009.
• Jesus F Vargas, Miguel A Ferrer, Carlos M Travieso, and Jesus B Alonso, “Off-line
Signature Verification Based on Psuedo-Cepstral Coefficients,” International Conference
on Document Analysis and Recognition, pp. 126-130, 2009
• V A Bharadi and H B Kekre, “Off-line Signature Recognition Systems,” International
Journal of Computer Applications, Vol. 1, No. 27, pp. 61-70, 2010
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