Recognition of Dynamic Signatures for People

Verification

Shern Yau and Dinesh Kant Kumar

School of Electrical and Computer Engineering,RMIT University Melbourne, Australia

Abstract. Machine based identity validation has applications such as

authentication of documents, for financial transactions, and for entry into

restricted space and database. The ineffectiveness of password and personal

identification numbers has been demonstrated by recent explosion of frauds.

This paper proposes the use of unpenned dynamic signature to validate the

authentic user and related transactional instruments. A comparison of the ability

of various classifiers for classifying the multi-dimensional features of the

dynamic signatures is reported. The technique has been tested for single user

and multi user and also when the forger is actively attempting to cheat the

system. The system is able to perfectly determine the authentic user from other

users when the user’s signature trace is secret. The system is also able to

perfectly reject forgers who may have access to the user’s signature, with a 10%

of the authentic user signature being classified as ‘unknown’.

1 Introduction

Our society is extremely conscious of security with an urgent need for securing

building space, data, and transactions. It is important to verify the identity and

authenticate an individual and related instruments of communication such as emails

and e-documents. There are three underlying principles for verifying the identity of an

individual- what they have (identity card), what they know (password) or what they

are (biometrics).

Penned signature is by far the most common person validation technique. This

combines the ‘what they know’ with ‘who they are’ and makes it a very effective tool

for person verification. Time immemorial, signatures have been used to validate

identity of people or authenticate documents and other such instruments. For less

sensitive applications such as validation of the signature is routinely conducted by lay

people such as people on the retail outlet check-out counter or teller of a bank or the

sentry of the building. Often the signatures are verified based on the visual

comparison with the sample of the authentic signature that is either kept on the back

of the card (credit card) or other similar instrument.

While the use of penned signatures in the traditional banks was not free from

frauds, the number of frauds was few because the bank executives were trained to be

able to spot forgery. But with the explosion of the use of credit cards there has been

an explosion of frauds. The database of the authentic signature are available at the

back of the card itself, and the check-out cashier are not trained to spot forgeries.

Yau S. and Kant Kumar D. (2007).

Recognition of Dynamic Signatures for People Veriﬁcation.

In Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems, pages 189-198

DOI: 10.5220/0002417601890198

 SciTePress

While graphic-analysis experts are able to spot the differences between authentic

signatures and frauds, a lay person at the counter of a departmental store is unable to

see such subtleties.

Use of Personal Identification Number (PIN) and password have evolved over

the past 3 decades to provide means of authentication of people for accessing funds,

database or buildings. These are being used at banks and ATM, for telephonic access

to financial and data, for accessing computers and database, and as a digital signature

to authenticate an electronic document. While this technique has the advantage that it

is easy to be automated, there are several shortcomings. Authentic users may forget

their PIN, and it has been demonstrated repeatedly that it is possible to deduce the

PIN from other seemingly unrelated information of the user.

In the recent past, biometrics techniques have been developed for machine

based verification of the identity of a person. Biometric authentication is based on

using some physiological or behavioural characteristic of a subject to authenticate that

particular subject [12]. Verification is defined as comparing an entity provided by the

user to a biometric template stored in the database.

Biometrics based verification techniques such as the use of DNA are suitable

for very high level of confidence, these techniques are not feasible for routine

applications such as logging into the computer, or accessing funds or paying at the

grocery store. Biometrics based techniques that are exceedingly being used include

the use of fingerprints, hand geometry and iris scan.

Even though the use of the anatomical measurements of the individual has

often been considered to be extremely robust for identifying an individual, these have

their own limitation. All traditional biometrics measures have certain limitations

associated to them [12]. DNA cannot be used in certain applications due to issues of

contamination, sensitivity, cumbersomeness and privacy; ear-shape as a biometric

measure has a problem of non unique features; facial biometrics have problems with

aging, face disguise and variable imaging conditions; hand and finger geometry can

be easily copied. Although fingerprints are very unique but they also have the

problem of fake fingers, storage and imaging conditions problems. Iris biometrics is

intrusive and has issues of unreliability. Speech biometrics has the limitation of

mechanical variance due to the microphone and dependence on subjects’ health [12].

The other major concern with the anatomical based biometrics is that these can be

copied by the impostor using deceit or force, and once copied, the authentic user

would be faced with life-long loss of identity.

To overcome some of the above mentioned shortcomings, researchers have

attempted to develop non-anatomical ‘biometrics’. Biometrics such as keystroke and

gait analysis are based on the behaviour of the individual [12] but the reliability is

highly questionable. The other shortcoming in each of these is that these do not give

the user any control.

This paper describes a machine based verification of identity that overcomes

the above mentioned difficulties. The system is based on the use of traditional

signatures, but without the user leaving a visual trace of the signature. The system

does not require any photograph, or any other physical or visual trace of the signature.

The unpenned signature verifier (USV) – captures the dynamics of the drawing of the

signature, which is much more difficult to copy even by an expert forger [10]. The

other advantage of this technique is that the data required to authenticate the USV is

small enough to be stored within an electronic document or on a smart-card.

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The features of the authentic user are a small vector (typical 30 bytes) and may

be stored on a database, a smart-card or a computer. The system has been designed

such that the user can configure the USV for his/ her signature, without requiring the

system administrator.

2 Theory

It is important [3] to seek identity verification modality which provides high degree in

performance and yet is still acceptable by a majority of users. Handwritten signatures

offer high degree in performance and are “yet a known and established legal status,

acceptability by the public, the elimination of common concerns about unwelcome

connotations or health factors associated with some other modalities, and the

convenience in execution afforded to users”. Handwritten signatures also offer the

biometrics measures as these are dependent on the user biometrics, while allowing the

users to change their signatures for suitable applications.

A signature can be authenticated either through static or dynamic verification.

These are discussed below:

Static: In this mode, the signature is written, either on a piece of paper and then

scanned or directly on the computer using devices such as the digital pad. The shape

of the signature is then compared with the authentic signature. This mode is also

known as “off-line” [4]. The difficulty with such a technique is that a good forger will

be able to copy the shape of the signature.

Dynamic: In this mode, the user writes his or her signature. This may happen in front

of a person, on camera or on a digitized tablet which acquires the signature in real-

time. By using this set of dynamic data, further information such as acceleration,

velocity, and instantaneous trajectory angels and displacements can be incurred [4].

To dynamically authenticate a signature, features are extracted from a temporal

domain scan of the user’s signature. These features form a template which is later

matched to the user’s enrolled signature template. A representative of the user’s

enrolled signature template needs to be available for the purpose of comparison in the

smart card [10] or encrypted with the document or message.

Some of the signature features currently used to determine the authenticity are

[1, 4, 13]:

• Azimuth and Altitude.

• Initial and final points.

• Writing speed -X & Y axis.

• Pen pressure.

• Pen-up and pen-down.

• Critical points.

• Pen position.

• Pen-tilt angle.

• Direction and pen movement.

This paper reports the use of the following features:

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• Time of pen-up and pen-down.

• Maximum Speed of pen movement.

• Total time to sign.

• Length of the signature.

• Maximum height of the signature.

• Maximum width of the signature.

3 Classification for Verification

Classification involves assigning of new inputs to one of a number of predefined

discrete classes. The complexity of a classification task is dependent on the variability

of the feature values of the observations of the same class relative to the difference

between feature values of the observations of different classes. The variability of the

feature values for inputs in the same class may be due to the underlying model of the

features or noise[2]. In a signature validation system, the noise associated with

classification of visual speech features is due to device, while the variation in the

signature of the authentic user is the underlying model variation. It is impossible for

classifiers to yield perfect performance and there is always an error rate due to the

misclassification of input patterns into wrong classes. However, it is desirable to keep

this error rate as low as possible to ensure the robustness of the applications.

Classification is performed via the partitioning of the multi-dimensional

feature space using statistical techniques or iterative learning algorithms. If the

features can be accurately classified by partitioning the feature space with

hyperplanes (or straight lines) are linearly separable. However, most of the real-world

pattern recognition applications involve non-linear partitioning of the feature space

where the surfaces dividing the feature space in the different class regions are

nonlinear[14]. Hence, such tasks would require the use of nonlinear classifiers.

Examples of linear and nonlinear classification techniques are Hidden Markov

Models (HMM), Artificial Neural Networks (ANN), Bayesian classifier, Support

Vector Machines (SVM) and cluster analysis.

These classification techniques can be further categorized into supervised and

unsupervised classifiers depending on the availability of training data. Supervised

classifiers are provided with training patterns with known class labels and exploit the

a-priori information of the training data. The unsupervised classifiers are not given

any training data with class labels. For such classifiers, the classification algorithms

attempt to find the underlying ‘similarities’ and group the ‘similar’ feature vectors in

one class. This paper reports the development of signature validation technique that is

user reconfigurable and hence requires a supervised classification system where the

targets are determined by the user.

There are number of possible classifiers that are available for classifying the

different inputs. The selection of the suitable classifier is very important to ensure the

success of the system. Some of the important classifiers are:

A. Bayesian Classifier: Bayesian classifier is a statistical classification technique

based on the Bayesian decision theory. The Bayesian classifier quantifies the different

classification decisions using probability and costs associated with such decisions.

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The Bayesian classifier assigns a feature vector to the ‘most probable’ class for the

feature vector. Such classifier relies on the assumption that the underlying probability

values of the input data are known [14]. It may be possible to determine the

probability function if the error of misclassification was random, but when attempting

to identify a fraud who is attempting to forge the signature, it is not possible to

estimate this probability and hence such classifiers are not suitable.

B. Hidden Markov Models (HMM): Hidden Markov Models (HMM) are one of most

commonly used classifiers for applications where the pattern is related to the temporal

variations. HMM can be applied to both discrete and continuous input signals.

Example of HMM-based classifiers are speech recognizers [11]. The main advantage

of using HMM classifiers for this applications is the ability of HMM in modelling the

temporal variation of non-stationary signals which could be important for classifying

the sequence of frames.

HMM models the sketching of the signature by characterizing only the

statistical properties of the signal. HMM technique assumes that the input signals can

be well characterized as a parametric random process known as Markov processes.

The difficulty with this approach is the complexity of the sketch and large number of

variables leading to a level of HMM that would be computationally impossible.

C. Artificial Neural Network: Artificial Neural Network (ANN) is an iterative

learning technique from the field of Artificial Intelligence (AI) that emulates the way

human’s biological nervous systems, such as the brain, processes information. ANNs

learn by examples provided to the network during training. An ANN can be

configured for data classification applications through a learning process. The major

advantage of using ANN is the non parametric nature of the network and also the

ability of ANN to classify data by without assumptions on the underlying statistical

distribution of the data. ANN can be made to generalize very well with sufficiently

large training sets. The parallel processing capabilities further encourage the use of

ANN in speech and image recognition where high computation rates are required and

the current systems are far from equalling human performance[8]. The ability of a lay

person to train the ANN to adapt and learn is important when designing a

reconfigurable system.

ANN models consist of a number of simple and highly interconnected

processing units known as neurons or nodes, which are analogous to the biological

neurons in the brain. The ANN model is composed of many nodes operating in

parallel and interconnected via numerical weights. The weights are iteratively

changed during training such that the ANN learns the features of the given input

classes. The nodes of ANN sum the weighted inputs and pass the results through a

nonlinear transfer function. The nodes of ANN can be characterized based on the

internal threshold and the type of transfer function used. Three common types of

transfer function used are; hard limiters, threshold logic elements (linear) and

sigmoidal nonlinearities. The architecture of an ANN can be varied and may consist

of a single or multiple layers of neurons.

Number of training algorithms have been developed for different applications

and ANN design. The training of ANN may be supervised or unsupervised.

Unsupervised ANN classifiers are self-learning and involve the partitioning of the

data in the feature space into subgroups where input and target pairs are not provided

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during training. Unsupervised ANN classifiers are related to clustering techniques.

Supervised ANN classifiers require training with suitable examples (input and target

pairs) to learn the patterns of each class[7].

4 Measure of Performance

The criterion for performance of the system is measured by the ability of the system

to identify the authentic user and reject the impostor. As with any security system,

given that the subject is, or is not a true instance of the enrolled subject, there are four

possible outcomes of the errors [2]. The accuracy of any biometric method may be

judged by four measures of error:

• Acceptance of Authentic Enrolled Subject (AA) or Genuine Accept Rate

(GAR)

• Acceptance of Impostor Subject (IA) or False Accept Rate (FAR)

• Rejection of Authentic Subject (RA) or False Reject Rate (FRR)

• Rejection of Impostor Subject (RI) or Genuine Impostor Rejection (GRR)

The biometric system accuracy requirements depend greatly on the application. In

forensic applications, such as criminal identification, FRR rate (and not FAR) is the

critical design issue, because we do not want to miss a criminal even at the risk of

manually examining a large number of potentially incorrect matches that the

biometric system identifies. In some cases the FAR might be one of the most

important factors in a highly secure access-control application, where the primary

objective is prevent impostors (e.g., at airports). Many civilian applications such as

digital signatures require the performance requirements to lie between these two

extreme limits of both FAR and FRR. The first (GAR) and the fourth (GRR)

identification rates are the main goals to test the efficacy of the method.

The application of the current system is to ensure that the impostor is rejected,

while minimising the rejection of the authentic user. It is thus desirable to have FAR

as close to zero as it is possible.

5 Method

This paper reports experimental verification of the unpenned signature verifier for two

applications. The first requires the system to identify the individual based on the

unpenned signature from the database. The second requires the system to determine

the authentic user when there are number of people attempting to forge the signature

of the authentic user. The experiments have been conducted using a digital tablet and

stylus (make ADC).

The method of person identification is divided into two separate modules: an

enrolment (or training) module and a recognition (or testing) module. The enrolment

module is responsible for enrolling new individuals in the system database. During

the enrolment phase, the individual supplies a number of samples of his/ her

signature. A model (developed iteratively) of the individual is built based on the

features extracted from the signature. During the recognition (test) phase, the

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individual repeats the signature. A measure of similarity is computed between the

features of the test signature with the signatures during enrolment. A multilayer

perceptron (MLP) of with 50 hidden and 1 output neurone was used for this purpose.

The size was determined iteratively. The efficacy of the technique is determined by

computing the Accuracy of Accepting an Enrolled Subject AAES (%) and Accuracy

of Rejecting the Impostor (ARI%).

5.1 Experimental Settings

This paper reports two sets of experiments; (i) identifying if a non-authentic user

could be classified as the user, when the signatures of the user are not known and (ii)

identifying the ability of the system to detect a forgery, when the fraud attempts to

copy the signature of the user. The experiments were conducted on 17 subjects. The

two set of experiments are described below:

Experiment 1. The first experiment required the subjects to sign on a digital tablet

and repeat the signature 30 times. The start of the signature was determined by the

pen-down instant, while the end of the signature was manually segmented. Each

subject was the ‘authentic user’ and against were the other people who also were

asked to record their signatures. Each sample of the signature were parameterised.

The parameters were then used to train the MLP that had the size of 90*50*1

neurones. After the training was completed, the user was made to repeat the signature

10 times. Other people were also asked to repeat their signatures own respective

signatures 10 times. These signatures were then used to test the system. The correct

classification of the authentic user and the ‘others’ was tabulated and used to

determine the performance of the system.

Experiment 2. In the second experiment the data of 60 signature samples were

considered. 30 of them were signed by one person – the authentic user, and the other

30 are signed by 5 other people (selected randomly) who were asked to copy the

signature of the first person. These 5 people were shown the signatures of the

authentic user, were given a trace of the signature and were requested to learn to copy

the authentic signature. 20 out of the 30 signatures were signed with the help of a

guide to help them sign as close as the reference signature as they could. The rest of

the signatures were signed by the ‘forger’ signers after they had practiced the

authentic signature. Lay people were unable to identify the difference from the trace.

From the pool of 30 samples belonging to each class, 20 out of 30 genuine and

forged signatures were chosen at random to be used as training data for the neural

network classifier. The balance 10 signatures of each of the class were used as test

samples.

The MLP output was given a threshold of 0.4 and 0.8, such that if the output

was less than 0.4 was classified as 0 and output greater than 0.8 as 1. An output

greater than 0.4 and less than 0.8 was classified as ‘unknown’.

For checking the efficacy of the approach Accuracy of Accepting an Enrolled

Subject AAES (%) and Accuracy of Rejecting an Impostor ARI (%) is calculated and

tabulated.

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6 Results and Discussion

The results of the experiments have been tabulated in tables 1, 2 and 3. Table 1 is

shows the output when the tests signatures were used on the trained neural networks.

Table 1. Results of Experiment 1- authentic user and other user.

ANN Output

Sample no. genuine Other

1 to17 100% 100%

From this table, it is observed that the genuine acceptance rate for the system when

the other users are not attempting to forge gives perfect results (100%). This

demonstrates that the ability of the system for low level security applications.

Table 2. Results of Experiment 2- Authentic user and Forger- 10 examples.

ANN Output

Classification

accuracy, threshold =

0.4, 0.8

sample

no. genuine forgery genuine forgery

1 0.941 0.0029

√ √

2 0.829 0.004

√ √

3 0.936 0.0029

√ √

4 0.574 0.119 Unknown

√

5 0.975 0.787

√

Unknown

6 0.5337 0.0729 Unknown

√

7 0.9917 0.3322

√ √

8 0.89 0.7943

√

Unknown

9 0.9585 0.0849

√ √

10 0.9505 0.0099

√ √

Table 3. Table of Genuine Acceptance Rate, False Acceptance Rate, False Reject Rate and

Genuine Reject Rate for Experiment 2.

Type percentage

GAR 90%

FAR 0%

FRR 0%

GRR 90%

From the table 2, it is observed that the system is robust and no forger is

classified as the authentic user. The robustness of the system is also demonstrated

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because it does not classify any authentic user as a forger. However the system does

classify 2 forgers and 2 authentic user signatures as Unknown. The False Acceptance

Rate and False Rejection rate is perfect (0%), the Genuine Acceptance Rate and

Genuine Rejection Rate is at 90%.

The results of the second experiment demonstrate that the system is suitable

for high level security applications where it is essential that FAR and FRR is 0, while

the GAR and GRR are ‘reasonable’.

7 Conclusion

The experiments results validate the use of unpenned dynamic signatures based

system for digital verification of the identity of the authentic user. The size of the data

required for the verification of a user is small and may easily be applied for validating

the author of related instruments of communication such as emails and e-documents.

Experiments were conducted to determine the ability of the system to (i)

recognise the signature from a database of signatures and (ii) determine the authentic

signature against forged signatures. The results indicate that the system is able to

recognise the authentic user with perfect accuracy from a small database of about 20

signatures. The system also demonstrates robustness when the other users actively

attempt to forge the authentic user’s signatures, even with the help of tracing guides.

The system has perfect False Acceptance Rate and False Reject Rate, suggesting its

suitability of being used for high security applications.

The system has used easy to obtain digital tablets, and utilises a simple neural

network that allows the user or user defined reconfiguration. The system provides the

user the ability to change their signature, and does not require a physical trace of the

signature, making it extremely secure.

Unlike some biometrics systems that are based on the anatomical measures of

the users, this system is user defined and controllable, and overcomes the

shortcomings of the biometrics techniques. It is based on time tested and accepted

penned signature and is expected to be acceptable across different national and

cultural boundaries, while providing similar level of reliability. It does not suffer from

other biometrics shortcomings such as the permanent loss of identity of an authentic

user as it empowers the user with the choice of the signature.

References

1. Dimauro, G., Impedovo, S., Lucchese, M.G., Modugno, R. and Pirlo, G., “Recent

Advancements in Automatic Signature Verification,” Frontiers in Handwriting

Recognition, 2004. IWFHR-9 2004. Ninth International Workshop, pp. 179 – 184, 2004.

2. Duda, R.O., Hart, P.E., and Stork, D.G., Pattern Classification: John Wiley & Sons Inc,

2001.

3. Fairhurst, M.C. and Kaplani, E., “Perceptual analysis of handwritten signatures for

biometric authentication”, Vision, Image and Signal Processing, IEE Proceedings, vol. 150,

no. 6, pp. 389 – 394, 2003.

197

4. Faundez-Zanuy, M., “Signature Recognition State-of-the-art,” Aerospace and Electronic

Systems Magazine, IEEE, vol. 20, no. 7, pp.28 - 32, 2005.

5. George Panotopoulos, D.P., "Hand Gesture Biometrics", Caltech Centre for Neuromorphic

Systems Engineering, 2001.

6. Jain, A. Griess F, and Connell D, "On-line signature verification," Pattern Recognit.,

vol.35, no.12, pp.2963–2972, Dec. 2002.

7. Kulkarni, A.D., Artificial Neural Networks for Image Understanding. New York: Van

Nostrand Reinhold, 1994.

8. Lippmann, R.P., “An Introduction to Computing with Neural Nets,” in IEEE ASSP

Magazine, 1987.

9. Nixon, M.S., et al., "Automatic Gait Recogntion", Biometrics: Personal Identification in

Networked society, pp. 231-250, 1999.

10. Plamondon, R., “The handwritten signature as a biometric identifier: psychophysical model

and system design,” Security and Detection 1995. European Convention, pp. 23 – 27, 1995.

11. Potamianos, G., Neti, C., Gravier, G., Garg, A., and Senior, A.W., “Recent Advances in the

Automatic Recognition of Audiovisual Speech,” Proceedings of the IEEE, vol. 91,

pp.1306-1324, 2003.

12. Prabhakar, S., Sharathpankanti. and A.K. Jain, "Biometric Recognition: Security and

Privacy Concerns", IEEE Security and Privacy, 2003.

13. Schmidt, C. and Kraiss, K.F., “Establishment of Personalized Templates for Automatic

Signature Verification,” Document Analysis and Recognition, 1997. Proceedings of the

Fourth International Conference, vol. 1, pp. 263 – 267, 1997.

14. Theodoridis, S., and Koutroumbas, K., Pattern Recognition: Academic Press, 1999.

198