Offline signature verification based on geometric feature extraction using artificial neural network

1. Offline Signature Verification
Based on
Geometric Feature Extraction
using -Artificial Neural Network
Guided by:
Ms. Lima Sebastian
Assistant Professor
CSE Dept. AISAT
Submitted by:
Cen Paul
S7 CSE
13027323

2. Overview
• Introduction
• Types of Signature Forgeries
• Workflow of the System
• Experimental Results
• Conclusion
• References
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3. Introduction
• For centuries, handwritten signatures have been an integral part of validating
business transactions , contracts and agreements.
• Among the different forms of biometric recognition systems such as
fingerprint, iris, face, voice, palm etc. , Handwritten signature is the most
widely used.
• In the era of advanced technology, security is the vital issue to avoid fakes
and forgeries.
• The signature verification is classified into online systems and offline
systems.
• The signature verification systems help to discriminate between the original
and fake signatures.
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4. Types of Signature Forgeries
1. Random Forgery
2. Simple Forgery
3. Skilled Forgery
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5. Block Diagram
Offline Signature
Verification System
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6. Workflow of Signature Verification
1. Data Acquisition
2. Preprocessing
3. Feature Extraction
4. Verification/Comparison
Input Data
Data
Preprocessing
Feature
Extraction
Comparison/
Verification
Forged or
Genuine?
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7. 1. Data Acquisition
• Signatures from individual person are taken on paper and then scanned with
scanner.
• The database contains data from individuals, including genuine signatures
and forgeries signatures.
• Signatures will be stored as images.
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8. 2. Preprocessing [1/4]
• Preprocessing is done for noise removal.
• Preprocessing stage includes :
i. RGB to gray scale conversion
ii. Binarization
iii. Cropping
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9. Preprocessing [2/4]
i. RGB to gray scale conversion
RGB image of scanned signature is converted into gray scale intensity signature
image to eliminate the hue and saturation information while retaining the
luminance.
RGB to Gray-scale Conversion
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10. Preprocessing [3/4]
ii. Binarization
A gray scale signature image is converted into binary image to count the number
of black pixels which make feature extraction simpler
Binarization
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11. Preprocessing [4/4]
iii.Cropping
Cropping the binary image using the boundary-values returned by bounding box
calculation method. This reduces the area of signature to be used for further
processing.
Cropping
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12. 3. Feature Extraction [1/4]
• To extract the feature of signature image using six global features.
• The extracted features of a signature image are based on geometrical
features like size and shape.
• Features used in this system :
i. Area
ii. Centroid
iii. Standard Deviation
iv. Skewness
v. Kurtosis
vi. Even-Pixels 12

13. Feature Extraction [2/4]
i. Area
Total number of black pixels present in the binary image.
ii. Centroid
It denotes to the center point of vertical and horizontal of the signature.
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14. Feature Extraction [3/4]
iii.Standard Deviation
It measures the amount of variation or dispersion on a set of mean data
values. If deviation is closed to the mean data value then the variation is less
otherwise spread over a wider range of values.
iv.Skewness
It measure the asymmetricity of the probability distribution of a real
valued random variable having positive, negative or may have undefined
value.
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15. Feature Extraction [4/4]
v. Kurtosis
Higher value of kurtosis distribution indicates thicker tails, longer and a
sharper peak whereas lower value denotes shorter, thinner tails.
In Image processing kurtosis values are illustrated in combination with
resolution and noise measurement. In which high kurtosis values gives low
noise and low resolution.
vi. Even Pixels
The positions in the image matrix. Even position refers those matrix
positions for which both the coordinates are even .
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16. 4. Verification
• The geometric feature are extracted and organised as an input array to the back
propagation network.
• The selected feature vectors are directed as input to the neural network.
• The trained neural network is used to verify the signature as either genuine or
forged.
• If the signature is match then it shows genuine otherwise forgery
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17. Experimental Results [1/4]
A. Database
• The signature database is collected from MCYT-75 offline signature corpus
database.
• 15 genuine and 15 forgery signature samples are given for each of 75 users in
database.
• The forgery signature in the database is the mixture of random, simple and
skilled forgeries.
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18. Experimental Results [2/4]
B. Performance Measures
• The performance measure of the signature verification is measured in terms of
false rejection rate (FRR) and false acceptance rate (FAR).
• False acceptance occurs when forgeries signatures are accepted as genuine
while in case of false rejection genuine signature are accepted as forgery.
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19. Experimental Results [3/4]
• Accuracy of the system is the mean between percentage of genuine signatures
verified as genuine and percentage of forgery signature is verified as forgery.
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20. Experimental Results [4/4]
C. Results
• Experiments were conducted on 18 different users. Each having 15 genuine
and 15 forgery signatures.
• Total number of 540 signature is taken each having dimension of 850 360
pixels.
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21. CONCLUSION
• Explored the application of geometric based feature extraction on offline
signature verification.
• The performance of the proposed method is examined using Back
propagation learning technique.
• Total accuracy obtained using the proposed method comes out to be above
89.24% .
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22. References
• Subhash Chandra , Sushila Maheskar . Offline signature verification based on
geometric feature extraction using artificial neural network .3rd Int’l Conf. on
Recent Advances in Information Technology RAIT-2016 .
• Mujahed Jarad, Nijad Al-Najdawi, and Sara Tedmori. Offline handwritten
signature verification system using a supervised neural network approach. In
Computer Science and Information Technology (CSIT), 2014 6th
International Conference on, pages 189–195. IEEE, 2014.
• R. Dubey and D. K. Agrawal, “Comparative analysis of off-line signature
recognition,” 2012 International Conference on Communication, Information
& Computing Technology (ICCICT), pp. 1–6, Oct. 2012.
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23. THANKYOU !!
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