23-08-2012, 12:54 PM
Signature Verification Using Static and Dynamic Features
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Abstract.
A signature verification algorithm based on static and dynamic features
of online signature data is presented. Texture and topological features are
the static features of a signature image whereas the digital tablet captures in
real-time the pressure values, breakpoints, and the time taken to create a signature.
1D - log Gabor wavelet and Euler numbers are used to analyze the textural
and topological features of the signature respectively. A multi-classifier decision
algorithm combines the results obtained from three feature sets to attain an
accuracy of 98.18%.
Introduction
Identification of an individual using behavioral biometrics is becoming prevalent and
includes online and offline signature verification. Online verification deals with both
static (number of black pixels, length and height of signature, etc) and dynamic features
(time taken and the speed of signing, etc) of the signature, while offline verification
extracts only the static features.
In this paper, we present an online signature verification algorithm which uses the
online and offline features extracted from data tablet. Online features such as pressure
values, breakpoints, and time taken to generate a signature are used to compute the
matching score. Signature pattern is generated using the data points extracted from the
tablet and then the static features, i.e. texture and topological features are analyzed to
perform the matching. 1D-log Gabor [1] is used to extract the textural features of the
signature pattern and Euler number is used to extract the topological features to compute
the matching score for the static features. The weighted sum rule based multiclassifier
decision algorithm combines the matching scores of online and offline features.
The following sections describe the algorithm in detail and discuss the experimental
results.
Signature Verification System
The block diagram of the signature verification system is shown in Figure 1. Forgery
of signatures can be classified as: a) Random forgery, where the forger randomly
guesses the signature, b) Skilled forgery, where the forger has prior knowledge of the signature and might have practiced in advance, and c) Tracing, where a signature
instance is used as a reference to attempt forgery. Most systems have high verification
rates for random forgery but low rates for skilled forgery and tracing. Our proposed
signature verification algorithm combines static and dynamic feature set to obtain a
high accuracy for both skilled forgery and tracing.
Data Preprocessing
The data acquisition process involves reading the reference signature data with the
help of a digitizing tablet and obtaining the dynamic parameters (pressure, breakpoints
and total time for a signature) and the image of the signature (Figure 1). Next,
the input data is preprocessed using a low-pass filter to eliminate spurious noise inherent
in the acquisition process [2].
Extraction of Static Features
The textural and topological features of a signature are extracted using algorithms
based on 1D log Gabor and Euler numbers respectively. The resultant image generated
by encoding the textural features is called Signature Code - log Gabor (SCLG).
Euler numbers give a vector matrix which contains values extracted from the topological
behavior of the signature. This vector matrix is called Signature Code Euler
(SCE).
Online Data Extraction
The parameters obtained from online signatures consist of pressure values, time, x-tilt,
y-tilt, x-value, y-value and breakpoints. In our algorithm, we use pressure, time and
breakpoints for matching purposes. Although x-tilt and y-tilt are two important online
features, these depend on holding style of the pen and orientation of the data tablet. Xvalues
and y-values generate the signature pattern (static) and are therefore not considered
again for online data matching.
Experimental Results
The proposed algorithm was tested using the signature database collected by the authors.
The database consists of 1,100 images from 110 different individuals. There are
660 genuine signatures (6 per person), 220 images of random forgery (2 per person),
110 images of skilled forgery (1 per person), and 110 images representing forgery by
tracing (1 per person). For training purposes, 330 genuine images from each individual
were are and the remaining 770 images are used as test data. Thresholds of different
values are determined by analyzing the results of the experiments. FAR-FRR
graphs are used to determine the optimal thresholds for best performance. The thresholds
of the four matching scores (MSSCLG, MSSCE, MSODM and MS) are found to be
0.650, 0.875, 0.935 and 0.823 respectively. Using these thresholds in the FAR-FRR
graph, the best performance and the accuracy of the multi-classifier decision algorithm
is determined. It has been found from the FAR-FRR graph that the proposed
algorithm gives the maximum accuracy of 98.18%. Table 1 shows the results obtained
from these algorithms.
Conclusion
In this paper a signature verification algorithm has been presented which uses the
static and dynamic features of the signature data. Signature data includes the signature
image, pressure values, number of breakpoints and time. The static behavior of the
signature is analyzed using 1D - log Gabor wavelet transform and Euler numbers and
matching is performed to obtain the matching scores for the offline features. Online
data are matched using statistical method and a matching score is calculated. Finally
weighted sum rule is used as multi-classifier decision algorithm for calculating the
final matching score which classifies the query data as matched or mismatched. The
experimental results show that this algorithm is robust to skilled forgeries and tracing
with an overall accuracy of 98.18%.