WICT 2008
Offline Handwritten Signature Verification
using
Radial Basis Function Neural Networks
George Azzopardi
Kenneth P. Camilleri
St. Martin’s Institute of IT
geazzo@gmail.com
St. Martin’s Institute of IT
Dept of Systems and Control Engineering
University of Malta
kpcami@eng.um.edu.mt
Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
Area of Focus and Objective
Area of Focus
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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Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
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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Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
Methodology
Pre-Processing
Data Area
Cropping
Width
Normalization
Binarization
Skeletonization
Feature Extraction and Image Vectorization
Global Features
Grid Features
Texture Features
Vector Quantization
Normalization
Classification
RBF Neural Network based on
Gaussian functions
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Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
Pre-Processing
Data Area Cropping
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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Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
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)
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Pd 1,0   34,6
Pd 1,1  39,0
Pd 0,1  40,0
Pd 1,1  36,34
Pd 1,0   34,6
Pd 1,1  39,0
Pd 0,1  40,0
Pd 1,1  36,34
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Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
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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Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
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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Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
Results
Best Results
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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Offline Handwritten Signature Verification using Radial Basis Function Neural Networks
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
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