1. WICT 2008
Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
Kenneth P. Camilleri
St. Martin’s Institute of IT
Dept of Systems and Control Engineering University of Malta
George Azzopardi

2. Area of Focus and Objective
Offline Handwritten Signature Verification (OHSV), Pattern Recognition, Behavioural Biometrics
Applications
Socially and legally accepted as a means of authentication
Financial Transactions, User Authentication, Passports, etc …
Motivation
Radial Basis Function Neural Networks (RBFNNs) are well-known for the robustness of outlier rejection
RBFNNs usually applied to Facial Expression and Face Classifications applications
RBFNN is applied by Baltzakis & Papamarkos (2001) within a two-stage neural network classifier signature verification technique
Objective
To investigate the viability of a single stage RBFNN for OHSV
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WICT 2008

3. Signature Database
No public Signature Database was available at the time of the study
Signature Acquisition
Recommendations by Mr. Joseph Gaffiero (a Maltese graphologist) and Dr. H. Baltzakis (expert in the field)
2492 signatures from 65 signers
40 signatures per signer (where possible)
25 on white blank sheets
15 within randomly-sized frames
5 different days
Different pens varying in colour and point type
Use as much intrapersonal skills as possible
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WICT 2008

4. Methodology
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WICT 2008

5. Pre-Processing Data Area Cropping Width Normalization Binarization
Segment the signature from background
Width Normalization
Signature image scaled (bicubic interpolation) to a constant width, keeping the aspect ratio fixed.
Binarization
24-bit image converted to grayscale and then binarized using a histogram-based binarization
Skeletonization
Thinning the signature without losing structural information
Facilitate the extraction of morphological features
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WICT 2008

6. Feature Extraction Based on three groups of features
Global Features (17 elements)
Information about the entire structure
Example
Signature Height, Height-To-Width ratio, etc …
Grid Features (576 elements)
Virtual grid of 8x12 cells
Pixel Density (1 feature), Pixel Distribution (4 features), Predominant Axial Slant (1 feature)
Texture Features (768 elements)
Same virtual grid of 8x12 cells
A 2x2 co-occurrence matrix is used to describe the transition of black and white pixels
Considered only p01 (black-to-white) and p11 (black-to-black) transitions (4x2 = 8 features)
14/11/2018
WICT 2008

7. Normalization & Vector Quantization
Global Features normalized in the range [0,1]
Get max of each global feature for all signatures and all signers
Divide each feature with the respective max
Vector Quantization used for Grid and Texture Features
K-Means Algorithm
Single codebook and 50 codewords
Classify all column vectors of all signatures and all signers
Replace each feature column vector (8x12) with the corresponding codeword
Normalize the quantized feature vectors
Grid Features
576-element grid feature vector
6 features x 12 columns x 8-element codewords
Texture Features
768-element texture feature vector
8 features x 12 columns x 8-element codewords
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WICT 2008

8. Classification X represents every signature with n features (elements)
n is dependent on the set of features applied
Global – n is 17
Grid – n is 576
Texture – n is 768
M is the number of signature models (signers); i.e. 65
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WICT 2008

9. Results Least effective features Best Results Texture Features
Combining Global and Grid features in a 593-element feature vector
Least effective features
Texture Features
FRR: 6.94% and FAR: 4.89%
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WICT 2008

10. Conclusion A single-stage RBFNN is an effective architecture for OHSV
Performance Results
TER: 4.08% MER: 2.04% FRR: 1.58% FAR: 2.5%
The performance compares well to results reported in the literature
Baltzakis & Papamarkos (2001) - 2-stage RBFNN
TER: 12.81% MER: 6.41% FRR: 3% FAR: 9.81%
Justino et al (2001) – HMM Classifier
MER: 2.135%
Future Work
Extending the system evaluation for simple and skilled forgeries
Using an adaptive technique to calculate the required number of codebooks and codewords for VQ
Investigating feature vector dimension reduction techniques
E.g. Principal Component Analysis
14/11/2018
WICT 2008