0A_PapersEnciclopediaBiometricsSignatureMarcosMartinez

of the implementation of the system, rather than

on weaknesses in the algorithms. For example, side-

channel attacks may use timing, power consumption,

or electromagnetic measurements on the security

device. Side-channel attacks are primarily of concern

for biometric encryption systems and match-on-card

devices where an attack could potentially be mounted

by iteratively improving the presented biometric. Very

little research has been done to explore the feasibil-

ity of side-channel attacks, but the success of attacks

on biometric template security and biometric encryp-

tion suggests that such attacks are certainly feasible.

Security and Liveness, Overview

Signal to Noise Ratio

Information is transmitted or recorded by variations in a

physical quantity. For any information storage or trans-

mission system, there will be intended variations in the

physical quantity signal and unintended variations

noise. In an analog telephone system, the signal (voice) is

represented by variation in a voltage level. As the signal is

transmitted along a phone line, it can pickup other

unintended variations e.g., leakage of other signals,

static from electrical storms that are noise. The ratio

of the signal level to the noise level is the signal to noise

ratio (SNR). Since it is a ratio, SNR is dimensionless.

However, SNR can be expressed as either an amplitude

ratio (voltage ratio for the phone example) or a power

ratio (milliWatts for the phone example). This leads to

confusion. SNR is frequently expressed as the log (base

10) of the ratio.When expressed as a log, the dimension-

less unit of SNR is decibel (dB).

What is signal and what is noise can depend on the

circumstances. Radio waves from lighting are noise to

an AM radio broadcast, but can be signal to a meteo-

rological experiment.

Iris Device

Signature Benchmark

Signature Databases and Evaluation

Signature Characteristics

Signature Features

Signature Corporate



MARCOS MARTINEZ-DIAZ, JULIAN FIERREZ

Biometric Recognition Group - ATVS,

Escuela Politecnica Superior, Universidad

Autonoma de Madrid, Campus de Cantoblanco,

Madrid, Spain

Synonyms

Signature benchmark; Signature corpora; Signature

data set

Definition

Signature databases are structured sets of collected

signatures from a group of individuals that are used

either for evaluation of recognition algorithms or as

part of an operational system.

Signature databases for evaluation purposes are, in

general, collections of signatures acquired using a

digitizing device such as a pen tablet or a touch-

screen. Publicly available databases allow a fair

performance comparison of signature recognition

algorithms proposed by independent entities. More-

over, signature databases play a central role in public

performance evaluations, which compare different

recognition algorithms by using a common experi-

mental framework. This type of databases is covered

in this entry.

On the other hand, signature databases can also be

a module of a verification or identification system.

1178S Signal to Noise Ratio

They store signature data and other personal informa-

tion of the enrolled users. This signature database is

used during the operation of the recognition system

to retrieve the enrolled data needed to perform the

biometric matching. This kind of databases is not

addressed here.

Dynamic Signature Databases

Until the beginning of this century, research on auto-

matic signature verification had been carried out using

privately collected databases, since no public ones were

available. This fact limits the possibilities to compare

the performance of different systems presented in the

literature, which may have been tuned to specific cap-

ture conditions. Additionally, the usage of small data

sets reduces the statistical relevance of experiments.

The lack of publicly available databases has also been

motivated by privacy and legal issues, although the

data in these databases are detached from any personal

information. The impact of the signature structural

differences among cultures must also be taken into

account when considering experimental results on a

specific database. As an example, in Europe, signatures

are usually formed by a fast writing followed by a

flourish, while in North America, they usually corre-

spond to the signers name with no flourish. On the

other hand, signatures in Asia are commonly formed

by Asian characters, which are composed of a larger

number of short strokes compared with European or

North American signatures.

While some authors have made public the data-

bases used for their experimental results [1], most

current dynamic signature databases are collected by

the joint effort of different research institutions. These

databases are, in general, freely available or can be

obtained at a reduced cost. Many signature databases

are part of larger multimodal biometric databases,

which include other traits such as fingerprint or face

data. This is done for two main reasons: the research

interest on multimodal algorithms and the low effort

required to incorporate the collection of other biomet-

ric traits once a database acquisition campaign has

been organized.

Two main modalities in signature recognition exist.

Off-line systems use signature images that have been

previously captured with a scanner or camera. On the

other hand, on-line systems employ digitized signals

from the signature dynamics such as the pen position

or pressure. These signals must be captured with spe-

cific devices such as pen tablets or touch-screens.

The most popular databases in the biometric research

community are oriented to on-line verification,

although in some of them, the scanned signature

images are also available [2, 3]. Some efforts have been

carried out in the handwriting community to collect

large off-line signature databases such as the GPDS-960

Corpus [4].

Unlike other biometric traits, signatures can be

forged with relative ease. Signature verification systems

must not only discriminate traits from different sub-

jects (such as fingerprints) but also must discriminate

between genuine signatures and forgeries. In general,

signature databases provide a number of forgeries for

the signatures of each user. The accuracy of the for-

geries depends on the acquisition protocol, the skill of

the forgers, and on how much time the forgers are let

to train. Nevertheless, forgeries in signature databases

are usually performed by subjects with no prior expe-

rience in forging signatures, this being a limitation to

the quality of forgeries.

Most on-line signature databases have been cap-

tured with digitizing tablets. These tablets are, in

general, based on an electromagnetic principle, allow-

ing the detection of the pen position (x,y), inclination

angles (y,g)(azimuth, altitude), and pressure p. Theyallow recording the pen dynamics even when the pen is

not in contact with the signing surface (i.e., during

pen-ups). On the other hand, databases captured

with other devices such as touch-screens (e.g., PDAs)

provide only pen position information, which is

recorded exclusively when the pen is in contact with

the device surface.

In the following, a brief description of the most

relevant available on-line signature databases is given

in chronological order.

PHILIPS Database

Signatures from 51 users were captured using a Philips

Advanced Interactive Display (PAID) digitizing tablet

at a sampling rate of 200 Hz [5]. The following signals

were captured: position coordinates, pressure, azi-

muth, and altitude.

Each user contributed 30 genuine signatures, leading

to 1,530 genuine signatures. Three types of forgeries are

present in the database: 1,470 over-the-shoulder for-

geries, 1,530 home-improved, and 240 professional

Signature Databases and Evaluation S 1179

S

forgeries. There is not a fixed number of forgeries avail-

able for each user. Over-the-shoulder forgeries were

produced by letting the forger observe the signing pro-

cess. Home-improved forgeries were produced by giving

to the forgers samples of the signature static image and

letting them to practice at home. Professional forgeries

were performed by forensic document examiners.

MCYT Bimodal Database

The MCYT bimodal database is comprised of signatures

and fingerprints from 330 individuals [2]. Signa-

tures were acquired using a Wacom Intuos A6 tablet

with a sampling frequency of 100 Hz. The users signed

repeatedly on a paper with a printed grid placed over the

pen tablet. The following time sequences are captured:

position coordinates, pressure, azimuth, and altitude.

There are 25 genuine signatures and 25 forgeries

per user, leading to 16,500 signatures in the database.

For each user, signatures were captured in groups of 5.

First, 5 genuine signatures, then 5 forgeries from an-

other user, repeating this sequence until 25 signatures

from each type, were performed. Each user provided 5

forgeries for the 5 previous users in the database. As

the user is forced to concentrate on different tasks

between each group of genuine signatures, the varia-

bility between groups is expected to be higher than the

one within the same group.

Genuine signatures and forgeries corresponding

to 75 users from the MCYT database were scanned

and are also available as an off-line signature database.

This signature corpus is one of the most popular for the

evaluation of signature verification algorithms that are

being used bymore than 50 research groups worldwide.

BIOMET Multimodal Database

The BIOMETmultimodal database [6] is comprised of

five modalities: audio (voice), face, hand, fingerprint,

and signature. The signatures were captured using

a Wacom Intuos2 A6 pen tablet and an ink pen with

a sampling rate of 100 Hz. The pen coordinates,

pen-pressure, azimuth, and altitude signals were cap-

tured. The database contains data from 84 users, with

15 genuine signatures and up to 12 forgeries per user.

Signatures were captured in two sessions separated by

35 months. In the first session, 5 genuine signatures

and 6 forgeries were acquired. The remaining 10

genuine signatures and 6 forgeries were captured in

the second session. Forgeries are performed by 4 dif-

ferent users (3 forgeries each). This database contains

2,201 signatures, since not all users have complete data:

8 genuine signatures and 54 forgeries are missing.

SVC2004 Database

Two signature databases were released prior to the

Signature Verification Competition (SVC) 2004 [7]

for algorithm development and tuning. They were

captured using a Wacom Intuos digitizing tablet and

a Grip Pen. Due to privacy issues, users were advised to

use invented signatures as genuine ones. Nevertheless,

users were asked to thoroughly practice their invented

signatures to reach a reasonable level of spatial and

temporal consistency.

The two databases differ in the available data, and

correspond to the two tasks defined in the competi-

tion. One contains only pen position information,

while the other provides pressure and pen orientation

(azimuth and altitude) signals also. Each database con-

tains 40 users, with 20 genuine signatures and 20 for-

geries per user acquired in two sessions, leading to

1,600 signatures per database. Forgeries for each user

were produced by at least four other users, aided by a

visual tool, which represented the signature dynamics

on a display. Both occidental and asian signatures are

present in the databases.

SUSIG Database

The SUSIG database consists of two sets: one cap-

tured using a pen tablet without visual feedback

(Blind subcorpus) and the other using a pen tablet

with an LCD display (Visual subcorpus) [8]. There are

100 users per database, but these do not coincide,

as the Visual subcorpus was captured 4 years after

the Blind one. For the Blind subcorpus, a WACOM

Graphire2 pen tablet was used. The Visual subcorpus

was acquired using an Interlink Electronics ePad-ink

tablet, with a pressure-sensitive LCD. In both subcor-

pora, the pen coordinates and the pen pressure signals

were captured using a sampling frequency of 100 Hz.

While performing forgeries, users had prior visual

input of the signing process on a separate screen or

on the LCD display for the Blind and Visual subcorpus

respectively.

1180S Signature Databases and Evaluation

For the Blind subcorpus, 8 or 10 genuine signatures

were captured in a single session. The users also

provided 10 forgeries from another randomly selected

user. Two sessions were performed in the Visual sub-

corpus. During each one, users provided 10 genuine

signatures and 5 forgeries.

MyIDea Multimodal Database

This signature set is a subset of the MyIDea Multimod-

al Biometric Database [9]. AWacom Intuos2 A4 graph-

ic tablet was used at a sampling rate of 100 Hz. Pen

position, pressure, azimuth, and altitude signals were

captured. This data set has the particularity that the

user must read loud what he is writing, allowing what

the authors call CHASM (Combined Handwriting and

SpeechModalities). This corpus consists of ca. 70 users.

Signatures were captured in 3 sessions. During each

session, each user performed 6 genuine signatures

and 6 forgeries, with visual access to the images of the

target signatures.

BiosecurID Multimodal Database

This database was collected by 6 different Spanish

research institutions [3]. It includes the following bio-

metric traits: speech, iris, face, signature, handwriting,

fingerprints, hand, and keystroke. The data were cap-

tured in 4 sessions distributed in a 4 month time span.

The user population was specifically selected to contain

a uniform distribution of users from different ages

and genders. Nonbiometric data were also stored,

such as age, gender, handedness, vision aids, and man-

ual worker (if the user has eroded fingerprints). This

allows studying specific demographic groups.

The signature pen-position, pressure, azimuth, and

altitude signals were acquired using a Wacom Intuos3

A4 digitizer at 100 Hz. During each session, two sig-

natures were captured at the beginning and two at the

end, leading to 16 genuine signatures per user. Each

user performed one forgery per session of signatures

from other three users in the database. The skill level

of the forgeries is increased by showing to the forger

more information of the target signature incremen-

tally. In the first session, forgers have only visual access

to one genuine signature; more data (i.e., signature

dynamics) are shown in further sessions and forgers

are let more time to train. Off-line signature data are

also available, since signatures were captured using an

inking pen.

BioSecure Multimodal Database

The BioSecure Multimodal Database was collected by

11 European institutions under the BioSecure Network

of Excellence [10]. It has three data sets captured in

different scenarios: DS1 was captured remotely over

the internet, DS2 was acquired in a desktop environ-

ment, and DS3 under mobile conditions. The database

covers face, fingerprint, hand, iris, signature, and

speech modalities and includes two signature subcor-

pora corresponding to the DS2 and DS3 data sets.

These two data sets were produced by the same group

of 667 users. The DS2 data set was captured using a

Wacom Intuos3 A6 digitizer at 100 Hz and an ink pen

while the user was sitting. On the other hand, the DS3

data set was captured with a PDA. Users were asked to

sign while standing and holding the PDA in one hand,

emulating realistic operating conditions. An HP iPAQ

hx2790 with a sampling frequency of 100 Hz was used

as capture device. The pen position, pressure, azimuth,

and altitude signals are available in DS2, while only the

pen position is available on DS3 due to the nature of

the PDA touch-screen.

Signatures were captured in two sessions and in

blocks of 5. An average of two months was left be-

tween each session. During each session, users were

asked to perform 3 sets of 5 genuine signatures and

5 forgeries between each set. Following this protocol,

each user performed 5 forgeries for the previous 4

users in the database. Thus, 30 genuine signatures and

20 forgeries are available for each user. Forgeries are

collected in a worst case scenario. For DS2, the

users had visual access to the dynamics of the signing

process of the signatures they had to forge on a com-

puter screen. In DS3, each forger had access to the

dynamics of the genuine signature on the PDA screen

and a tracker tool allowing to see the original strokes.

Some users were even allowed to sign following the

strokes produced by the tracker tool, reproducing

thus the geometry and dynamics of the forged signa-

ture with high accuracy.

The DS3 data set is the first multisession database

captured on a PDA and represents a very challenging

database [11]. Apart from the high accuracy of the


S

forgeries, signatures from DS3 present sampling errors

and irregular sampling rates. Moreover, pen posi-

tion signals during pen-ups are not available, since

the acquisition device captures the pen dynamics only

when the PDA stylus is in contact with the touch-

screen surface.

The capture process for both DS2 and DS3 is shown

in Fig. 1. Examples of signatures from the BioSecure

Signature subcorpora corresponding to DS2 and DS3

are presented in Fig. 2. Unconnected samples represent

that at least one sample is missing between them due to

sampling errors.

In Table 1, the main features of the described sig-

nature databases are presented.

Signature Verification EvaluationCampaigns

Despite the usage of a common database, one of the

main difficulties when comparing the performance of

different biometric systems is the different experimen-

tal conditions, under which each system is evaluated by

its designers. To overcome these difficulties, evalua-

tions and competitions provide a common reference

for system comparison on the same database and pro-

tocol. Public evaluations in the field of automatic sig-

nature verification are less common than for other

biometric modalities such as fingerprint or speech.

In particular, only evaluations for the on-line signature

verification modality have been proposed. These in-

clude the Signature Verification Competition (SVC),

which took place in 2004 [7], the signature modality of

the BioSecure Multomodal Evaluation Campaign held

in 2007 [12], and the BioSecure Signature Evaluation

Campaign in 2009 [13].

Signature Verification Competition(SVC 2004)

The Signature Verification Competition (SVC 2004)

represents the first public evaluation campaign in the

field of signature verification [7]. The competition was

divided into two tasks, depending on the available

signature signals. In Task 1, only the pen position

signals (x,y) and the sample timestamps were available.

In Task 2, the pen pressure p and azimuth and altitude

angles (y,g) were also available. Participants had prioraccess to a signature dataset for each task. These data

sets were later released for public access, and are

referred to as the SVC2004 database. Signatures from

40 users are present in each data set. This evaluation

has the particularity that users were advised to use

invented signatures because of privacy issues. More-

over, they did not have visual feedback from the sign-

ing process, since signatures were captured with a

digitizing tablet and a special pen.

The evaluation results were first released to par-

ticipants, which then had the choice to remain anony-

mous. The best Equal Error Rate (EER) in Task 1

was of 2.84% against skilled forgeries and 1.85%

for random forgeries. In Task 2 (which included

pressure and pen-inclination signals), the lowest

EERs were 2.89% against skilled forgeries and 1.70%

against random forgeries.

Signature Databases and Evaluation. Figure 1 PDA signature capture process in the BioSecure DS3 - Mobile Scenario

dataset (left) and pen-tablet capture process in the BioSecure DS2 - Access Control Scenario dataset (right). The

acquisition setup and paper template used in DS2 is similar to the ones used in MCYT, BIOMET, MyIDea and BiosecurID.


BioSecure Multimodal EvaluationCampaign (BMEC 2007)

The BioSecure Multimodal Evaluation Campaign

(BMEC) was held in 2007 with the aim of compar-

ing the performance of verification systems from

different research groups on individual biometric

modalities and fusion scenarios [14]. Two scenarios

were considered: access control and mobile condi-

tions. In particular, the Mobile Scenario consisted of

four modalities and fusion, using a subset of the

BioSecure Multimodal Database DS3 captured on

mobile conditions (i.e., using portable devices such

as a PDA).

Signature Databases and Evaluation. Figure 2 Examples of signatures and associated signals from the BioSecure

Multimodal Database DS2 and DS3 signature subcorpora captured using a pen tablet (top) and a PDA (bottom),

respectively. As can be seen, there are missing samples for the signature captured with PDA, and no signals are available

during pen-ups, contrary to the pen-tablet case.


S

In this evaluation, a signature subset from the Bio-

Secure Multimodal DS3 database was used. A set of

signatures from 50 users was previously released to

participants for algorithm development and tuning.

For each user, 20 genuine signatures (15 from the first

session and 5 from the second) as well as 20 forgeries

were available.

Eleven signature verification systems from seven

independent European research institutions were pre-

sented to the evaluation. The results of the evaluation

and a description of each system that participated can

be found in [12]. Another evaluation study in similar

conditions, including a comparative analysis with

respect to the BMEC participants, can be found in

[11]. The best Equal Error Rate (EER) in the evalua-

tion was of 4.03% for random forgeries and of 13.43%

for skilled forgeries. The relatively high EER for skilled

forgeries reveals the high quality of the forgeries

acquired in this database.

BioSecure Signature Evaluation Campaign(BSEC 2009)

The BioSecure Signature Evaluation Campaign is

aimed at measuring the impact of mobile acquisition

conditions, time variability, and the information con-

tent of signatures in the performance of verification

algorithms [13]. Signature subsets from the BioSecure

Multimodal Databases DS2 (pen tablet) and DS3 (PDA

touch-screen) corresponding to 50 users have been

released to participants prior to the evaluation. At the

time of publication, the results of the evaluation cam-

paign are still not available.

Related Entries

Biometric Sample Acquisition

Off-line signature verification

Performance Evaluation, Overview

Signature Recognition

References

1. Munich, M.E., Perona, P.: Visual identification by signature

tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(2),

200217 (2003)

2. Ortega-Garcia, J., Fierrez-Aguilar, et al.: MCYT baseline corpus:

a bimodal biometric database. IEE Proc. Vis. Image Signal Pro-

cess. 150(6), 391401 (2003)

3. Fierrez, J., Galbally, J., Ortega-Garcia, J., Freire, M.R., Alonso-

Fernandez, F., Ramos, D., Toledano, D.T., Gonzalez-Rodriguez,

J., Siguenza, J.A., Garrido-Salas, J., Anguiano-Rey, E., de Rivera,

G.G., Ribalda, R., Faundez-Zanuy, M., Ortega, J.A., Cardenoso-

Payo, V., Viloria, A., Vivaracho, C.E., Moro, Q.I., Igarza, J.J.,

Sanchez, J., Hernaez, I., Orrite-Urunuela, C., Martinez-Contreras,

F., Gracia-Roche, J.J.: Biosecurid: A multimodal biometric data-

base. Pattern Analysis & Applications (to appear) (2009)

Signature Databases and Evaluation. Table 1 Summary of the most popular on-line signature databases. The symbols

x,y,p,y,g denote pen position horizontal coordinate, vertical coordinate, pen pressure, azimuth and altitude respectively

Name Device Users Sessions

Signatures per user

Signals Interval between sessionsGenuine Forgeries

PHILIPS Pen tablet 51 35 30 up to 70 x,y,p,y,g 1 week approx.

BIOMET Pen tablet 84 3 15 up to 12 x,y,p,y,g 35 months

MCYT Pen tablet 330 1 25 25 x,y,p,y,g -

SVC2004 Task 1 Pen tablet 40 2 20 20 x,y min. 1 week

SVC2004 Task 2 Pen tablet 40 2 20 20 x,y,p,y,g min. 1 week

SUSIG Blind Subcorpus Pen tablet 100 1 8 or 10 10 x,y,p -

SUSIG Visual Subcorpus Pen tablet 100 2 20 10 x,y,p 1 week approx.

MyIDea Pen tablet ca. 100 3 18 18 x,y,p,y,g days to months

BioSecurID Pen tablet 400 4 16 16 x,y,p,y,g 1 month approx.

BioSecure DS2 Pen tablet ca. 650 2 30 20 x,y,p,y,g 1 month approx.

BioSecure DS3 PDA ca. 650 2 30 20 x,y,p,y,g 1 month approx.


4. Vargas, J., Ferrer, M., Travieso, C., Alonso, J.: Off-line hand-

written signature GPDS-960 corpus. In: Proceedings of ninth

International Conference on Document Analysis and Recogni-

tion, ICDAR, vol. 2, pp. 764768. Curituba, Brazil (2007)

5. Dolfing, J.G.A., Aarts, E.H.L., van Oosterhout, J.J.G.M.:

On-line signature verification with Hidden Markov Models.

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Australia (1998)

6. Garcia-Salicetti, S., Beumier, C., Chollet, G., Dorizzi, B., Jardins,

J.L.L., Lanter, J., Ni, Y., Petrovska-Delacretaz, D.: BIOMET: A

multimodal person authentication database including face,

voice, fingerprint, hand and signature modalities. In: Proceed-

ings of IAPR International Conference on Audio- and Video-

based Person Authentication, AVBPA, pp. 845853. Springer

LNCS-2688. Brisbane, Australia (2003)

7. Yeung, D.Y., Chang, H., Xiong, Y., George, S., Kashi, R.,

Matsumoto, T., Rigoll, G.: SVC2004: First international signa-

ture verification competition. In: Proceedings of International

Conference on Biometric Authentication, ICBA, pp. 1622.

Springer LNCS-3072 (2004)

8. Kholmatov, A., Yanikoglu, B.: Newblock Susig: an on-line signa-

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Humm, A., Evequoz, F., Ingold, R., Rotz, D.V.: MyIDea -

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10. Association BioSecure: BioSecure multimodal database. (http://

www.biosecure.info) (2007). Last Accessed 03 March, 2009

11. Martinez-Diaz, M., Fierrez, J., Galbally, J., Ortega-Garcia, J.:

Towards mobile authentication using dynamic signature verifi-

cation: useful features and performance evaluation. In: Proc.

Intl. Conf. on Pattern Recognition, ICPR pp. 16 (2008)

12. TELECOM&Management SudParis: BioSecure Multimodal Eval-

uation Campaign 2007 Mobile Scenario - experimental results.

Tech. rep. (2007). (http://biometrics.it-sudparis.eu/BMEC2007/

files/Results_mobile.pdf). Last Accessed 03 March, 2009

13. TELECOM & Management SudParis: Biosecure Signature

Evaluation Campaign, BSEC 2009. http://biometrics.it-sudparis.

eu/BSEC2009. URL http://biometrics.it-sudparis.eu/BSEC2009

14. Alonso-Fernandez, F., Fierrez, J., Ramos, D., Ortega-Garcia, J.:

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USA (2008)

Signature Dataset


Signature Features

MARCOS MARTINEZ-DIAZ1, JULIAN FIERREZ1,

SEIICHIRO HANGAI2

1Biometric Recognition Group - ATVS, Escuela

Politecnica Superior, UniversidadAutonoma deMadrid,

Campus de Cantoblanco, Madrid, Spain2Department of Electrical Engineering, Tokyo

University of Science, Japan

Synonyms

Signature characteristics

Definition

Signature features represent magnitudes that are extrac-

ted from digitized signature samples, with the aim of

describing each signature as a vector of values. The

extraction and selection of optimum signature features

is a crucial step when designing a verification system.

Features must allow each signature to be described in a

way that the discriminative power between signatures

produced by different users is maximized while allowing

variability among signatures from the same user.

On-line signature features can be divided into two

main types. Global features model the signature as

a holistic multidimensional vector and represent mag-

nitudes such as average speed, total duration, and

aspect ratio. On the other hand, local features are

time-functions derived from the signals, such as the

pen-position coordinate sequence or pressure signals,

captured with digitizer tablets or touch-screens.

In off-line signature verification systems, features

are extracted from a static signature image. They can

also be classified as global, if they consider the image as

a whole (e.g., image histogram, signature aspect ratio);

or local, if they are obtained from smaller image

regions (e.g., local orientation histograms).

This entry is focused on on-line signature features,

although a brief outline of off-line signature features

is also given.

Introduction

Several approaches to extract discriminative informa-

tion from on-line signature data have been proposed

Signature Features S 1185

S

in the literature [1]. The existing systems can be broadly

divided into two main types: Global systems, in which a

holistic vector representation consisting of a set of

global features (e.g., signature duration, direction

after first pen-up) is derived from the signature trajec-

tories [2, 3], and function-based systems, in which time

sequences describing local properties of the signature

are used for recognition [4, 5], (e.g., position, acceler-

ation). Although recent works show that global

approaches are competitive with respect to local meth-

ods in some circumstances [6], the latter approach has

traditionally yielded better results. Despite this advan-

tage, systems based on local features usually employ

matching algorithms, which are computationally more

expensive than global-feature ones.

Due to the usually low amount of training data

in signature verification, feature selection techniques

must be applied in order to reduce the feature vector

dimensionality. These techniques allow of finding the

optimal feature set for each system or scenario [7].

Feature extraction and preprocessing

Signature features are, in general, extracted from

the time functions captured from the pen dynamics

while an individual signs. In most cases, the capture

of time functions from the handwritten signature

is carried out with acquisition devices such as digitiz-

ing tablets or touch-screens. These devices provide

pen position information (i.e. horizontal x and verti-

cal y coordinates), and in some cases, pen pressure

z and pen inclination ( azimuth and altitude).

A diagram showing the nature of the captured signals

and an example of the signals from a real signature

are shown in Fig. 1. Other less common examples of

on-line signature acquisition devices are special pens

with dedicated hardware inside that captures signa-

ture data such as coordinate, force, or velocity

information.

The sampling rate of these devices is, in general, bet-

ween 100 and 200 Hz. Since the maximum frequencies

of the pen movements during handwriting are 20-30 Hz

[1], these sampling rates satisfy the Nyquist criterion.

Preprocessing steps before feature extraction may

be performed, such as position, size and rotation nor-

malization, noise filtering, or resampling. In some

works, resampling is avoided as it degrades the velocity

related features [4].

Global features

Global feature-based systems describe each signature

as a multidimensional vector where each element

consists on a feature extracted from the whole pen

trajectory. Many feature sets have been proposed in

the literature [2, 3, 8, 9], with variable sizes and a

maximum size of 100 features [6]. Due to the train-

ing data scarcity and adverse effects of the curse of

dimensionality, feature selection techniques must be

applied to reduce the feature vector size. In Table 1,

the 100 features described in [6] are presented.

This global feature set includes most of the features

described in previous works from other authors.

Features are arranged in the order of descending

individual discriminative power. In Fig. 2, examples

of the distribution of global features presented in

Table 1 are shown.

Local features

Local features represent time sequences extracted

from the signature raw captured data. A set of local

features leads to a multidimensional discrete se-

quence that describes a signature. Depending on the

matching algorithm, feature sets of varying sizes have

been proposed in the literature. As a rule of thumb,

Dynamic Time Warping-based algorithms employ

few local features, while systems based on Hidden

Markov Models or Gaussian Mixture Models employ

larger feature sets. In Table 2, the most popular local

features found in the literature are presented [2, 3, 4, 5,

10, 11, 12].

As in the case of global features, feature selection

algorithms must be applied to discriminate the best

performing feature set. Usually, small feature sets are

selected for Dynamic Time Warping-based matching

algorithms. In these systems, speed-related features

extracted from the first derivative of the pen-coordi-

nate time sequences (features 10-11 in Table 2) have

shown to be very effective [4]. On the other hand,

larger feature sets are used when Hidden Markov or

Gaussian Mixture Models are employed [5, 11] for

signature matching. Features related to second-order

derivatives (features 19-27 in Table 2) have not proved

to significantly improve the system verification perfor-

mance [3]. Examples of the local features presented in

Table 2 are depicted in Fig. 3.

1186S Signature Features

The usage of features related to pen orientation

(azimuth and altitude) is a subject of controversy.

Although some authors report that these features in-

crease the verification performance [12], others have

reported a low discriminative power for these features

[2]. Moreover, these features are not always available,

since many touch-screen acquisition devices such as

Tablet-PCs or PDAs are unable to capture pen orienta-

tion information.

The fusion of the global and local feature-based

systems has been reported to provide better perfor-

mance than the individual systems [6].

Signature Features. Figure 1 (a) Representation of the position, azimuth and altitude of the pen with respect to the

capture device. (b) Example of raw captured data from a signature.


S

Signature Features. Table 1 Set of global features sorted by individual discriminative power (T denotes time interval,

t denotes time instant, N denotes number of events, y denotes angle. Note that some symbols are defined in differentfeatures of the table (e.g., D in feature 7 is defined in feature 15)

Ranking Feature Description Ranking Feature Description

1 signature total duration Ts 2 N(pen-ups)

3 N(sign changes of dx dt and dy dt) 4 average jerk j

5 standard deviation of ay 6 standard deviation of vy

7 (standard deviation of y)/Dy 8 N(local maxima in x)

9 standard deviation of ax 10 standard deviation of vx

11 jrms 12 N(local maxima in y)

13 t(2nd pen-down) Ts 14 (average velocity v)/vx,max15 Aminymaxyminxmaxxmin

DxPpendowns

i1 xmax jixmin jiDy16 (xlast pen-upxmax) Dx

17 (x1st pen-downxmin) Dx 18 (ylast pen-upymin) Dy19 (y1st pen-downymin) Dy 20 (Twv) (ymaxymin)21 (Twv) (xmaxxmin) 22 (pen-down duration Tw)/Ts23 v vy,max 24 (ylast pen-upymax) Dy25 Tdy=dt=dx=dt>0

Tdy=dt=dx=dt0 jpen-up) Tw

33 N(vx0) 34 direction histogram s135 (y2nd local maxy1st pen-down) Dy 36 (xmaxxmin)/xacquisition range37 (x1st pen-downxmax) Dx 38 T(curvature>Thresholdcurv) Tw39 (integrated abs. centr. acc. aIc)/amax 40 T(vx>0) Tw41 T(vx0 jpen-up) Tw43 (x3rd local maxx1st pen-down) Dx 44 N(vy0)45 (acceleration rms a)/amax 46 (standard deviation of x)/Dx47 Tdx=dtdy=dt>0

Tdx=dtdy=dt

Signature Features. Table 1 (Continued)

Ranking Feature Description Ranking Feature Description

83 jy,max 84 y(2nd pen-down to 2nd pen-up)

85 jmax 86 spatial histogram t3

87 (1st t(vy,min))/Tw 88 (2nd t(xmax))/Tw

89 (3rd t(xmax))/Tw 90 (1st t(vy,max))/Tw

91 t(jmax) Tw 92 t(jy,max) Tw93 direction change histogram c2 94 (3rd t(ymax))/Tw

95 direction change histogram c4 96 jy

97 direction change histogram c3 98 y(initial direction)

99 y(before last pen-up) 100 (2nd t(ymax))/Tw

Signature Features. Figure 2 Examples of genuine signatures and forgeries (left) and scatter plots of 4 different

global features from the 100-feature set presented in Table 1 (right). The signatures belong to the BioSecure database

and the Figure has been adapted from [13].


S

Off-line signature features

Off-line signature verification systems usually rely

on image processing and shape recognition techni-

ques to extract features. As a consequence, additional

preprocessing steps such as image segmentation and

binarization must be carried out. Features are

extracted from gray-scale images, binarized images,

or skeletonized images, among other possibilities.

The proposed feature sets in the literature are nota-

bly heterogeneous, specially when compared with the

case of on-line verification systems. These include,

among others, the usage of image transforms (e.g.,

Hadamard), morphological operators, structural

representations, graphometric features [14], direc-

tional histograms, and geometric features. Readers are

referred to [15] for an exhaustive listing of off-line

signature features.

Related Entries

Feature Extraction

Off-line Signature Verification

On-line Signature Verification

Signature Matching


References

1. Plamondon, R., Lorette, G.: Automatic signature verification

and writer identification: the state of the art. Pattern Recogn.

22(2), 107131 (1989)

2. Lei, H., Govindaraju, V.: A comparative study on the consistency

of features in on-line signature verification. Pattern Recogn. Lett.

26(15), 24832489 (2005)

Signature Features. Table 2 Extended set of local features. The upper dot notation (e.g., xn) indicates time derivative

# Feature Description

1 x-coordinate xn

2 y-coordinate yn

3 Pen-pressure zn

4 Path-tangent angle ynarctan(yn xn)5 Path velocity magnitude un

_yn _xn

p6 Log curvature radius rn log(1 kn) log(n _yn), where kn is the curvature of the

position trajectory

7 Total acceleration magnitude an t2n c2n

p _u2n u2ny2nq , where tn and cn are respectively thetangential and centripetal acceleration components of the penmotion

8 Pen azimuth gn9 Pen altitude fn1018 First-order derivative of features 19 xn, yn, zn, _yn, _un, _rn, an, _gn, _fn1927 Second-order derivative of features 19 xn,yn,zn,yn,un,rn,an,gn, fn28 Ratio of the minimum over the maximum

speed over a window of 5 samplesnrmin {n4, . . . ,n} max {n4, . . . ,n }

2930 Angle of consecutive samples and firstorder difference

anarctan(ynyn1 xnxn1) _an

31 Sine snsin(an)32 Cosine cncos(an)33 Stroke length to width ratio over a window

of 5 samples r5n Pknkn4

xkxk12ykyk12

p

max xn4;:::;xnf gmin xn4 ;:::;xnf g34 Stroke length to width ratio over a window

of 7 samples r7n Pknkn6

xkxk12ykyk12

p

max xn6 ;:::;xnf gmin xn6;:::;xnf g

1190S Signature Features

3. Richiardi, J., Ketabdar, H., Drygajlo, A.: Local and global feature

selection for on-line signature verification. In: Proceedings of

IAPR eighth International Conference on Document Analysis

and Recognition, ICDAR, Seoul, Korea (2005)

4. Kholmatov, A., Yanikoglu, B.: Identity authentication using im-

proved online signature verification method. Pattern Recogn.

Lett. 26(15), 24002408 (2005)

5. Fierrez, J., Ramos-Castro, D., Ortega-Garcia, J., Gonzalez-

Rodriguez, J.: HMM-based on-line signature verification: feature

extraction and signature modeling. Pattern Recogn. Lett. 28(16),

23252334 (2007)

6. Fierrez-Aguilar, J., Nanni, L., Lopez-Penalba, J., Ortega-Garcia, J.,

Maltoni, D.: An on-line signature verification system based on

fusion of local and global information. In: Proceedings of IAPR

Signature Features. Figure 3 Examples of functions from the 27-feature set presented in Table 2 for a genuine signature

(left) and a forgery (right) of a particular subject.


S

International Conference on Audio- and Video-Based Biometric

Person Authentication, AVBPA, Springer LNCS-3546, pp. 523532

(2005)

7. Jain, A.K., Zongker, D.: Feature selection: evaluation, applica-

tion, and small sample performance. IEEE Trans. Pattern Anal.

Mach. Intell. 19(2), 153158 (1997)

8. Nelson, W., Turin, W., Hastie, T.: Statistical methods for on-line

signature verification. Int. J. Pattern Recogn. Artif. Intell. 8(3),

749770 (1994)

9. Lee, L.L., Berger, T., Aviczer, E.: Reliable on-line human signa-

ture verification systems. IEEE Trans. Pattern Anal. Mach. Intell.

18(6), 643647 (1996)

10. Dolfing, J.G.A., Aarts, E.H.L., van Oosterhout, J.J.G.M.: On-line

signature verification with Hidden Markov Models. In: Proceed-

ings of the International Conference on Pattern Recognition,

IEEE Press, pp. 13091312 (1998)

11. Van, B.L., Garcia-Salicetti, S., Dorizzi, B.: On using the Viterbi

path along with HMM likelihood information for online signa-

ture verification. IEEE Trans. Syst. Man Cybern. B 37(5),

12371247 (2007)

12. Muramatsu, D., Matsumoto, T.: Effectiveness of pen pressure,

azimuth, and altitude features for online signature verification.

In: Proceedings of IAPR International Conference on

Biometrics, ICB, Springer LNCS 4642 (2007)

13. Martinez-Diaz,M., Fierrez, J., Galbally, J., Ortega-Garcia, J.: Towards

mobile authentication using dynamic signature verification: use-

ful features and performance evaluation. In: Proceedings Interna-

tional Conference on Pattern Recognition, ICPR, pp. 16 (2008)

14. Sabourin, R.: In: Off-line signature verification: recent advances

and perspectives. Lect. Notes Comput. Sci. 1339 8498 (1997)

15. Impedovo, D., Pirlo, G.: Automatic signature verification: The

state of the art. IEEE Trans. Syst. Man Cybern. C Appl. Rev.

38(5), 609635 (2008)

Signature Matching

MARCOS MARTINEZ-DIAZ1, JULIAN FIERREZ1,

SEIICHIRO HANGAI2

1Biometric Recognition Group - ATVS, Escuela

Politecnica Superior, Universidad Autonoma de

Madrid, Madrid, Spain2Department of Electrical Engineering Tokyo

University of Science, Japan

Synonyms

Signature similarity computation

Definition

The objective of signature matching techniques is to

compute the similarity between a given signature and a

signature model or reference signature set. Several pat-

tern recognition techniques have been proposed as

matching algorithms for signature recognition. In on-

line signature verification systems, signature matching

algorithms have followed two main approaches. Fea-

ture-based algorithms usually compute the similarity

among multidimensional feature vectors extracted

from the signature data with statistical classification

techniques. On the other hand, function-based algo-

rithms perform matching by computing the distance

among time-sequences extracted from the signa-

ture data with technique such as Hidden MarkovMod-

els and Dynamic Time Warping. Off-line signature

matching has followed many different approaches,

most of which are related to image processing and

shape recognition.

This essay focuses on on-line signature matching,

although off-line signature matching algorithms are

briefly outlined.

Introduction

As in other biometric modalities, signature matching

techniques vary depending on the nature of the features

that are extracted from the signature data. In feature-

based systems (also known as global), each signature

is represented as a multidimensional feature vector,

while in function-based systems (also known as local)

signatures are represented by multidimensional time

sequences. Signature matching algorithms also depend

on the enrollment phase.Model-based systems estimate

a statistical model for each user from the training

signature set. On the other hand, in reference-based

systems the features extracted from the set of training

signatures are stored as a set of template signatures.

Consequently, given an input signature, in model-

based systems the matching is performed against a

statistical model, while in reference-based systems the

input signature is compared with all the signatures

available in the reference set.

Feature-Based Systems

Feature-based systems usually employ classical pattern

classification techniques. In reference-based systems, the

matching score is commonly obtained by using a dis-

tance measure between the feature vectors of input and

template signatures [1, 2], or a trained classifier. Distance

1192S Signature Matching

measures used for signature matching include Eucli

dean, weighted Euclidean, and Mahalanobis distance. In

model-based systems, trained classifiers are employed,

including approaches such as Neural Networks, Gaussian

Mixture Models [3] or Parzen Windows [4].

Function-Based Systems

In these systems, multidimensional time sequences

extracted from the signature dynamics are used as fea-

tures. Given the similarity of this task to others related to

speaker recognition, the most popular approaches

in local signature verification are related to algorithms

proposed in the speech recognition community.

Among these, signature verification systems using

Dynamic Time Warping (DTW) [5, 6, 7] or Hidden

Markov Models (HMM) [8, 9, 10, 11] are the most

popular approaches in signature verification. In such

systems, the captured time functions (e.g., pen coordi-

nates, pressure, etc.) are used to model each user sig-

nature. In the following, Dynamic Time Warping and

Hidden Markov Models are outlined. An brief over-

view of other techniques is also given.

Dynamic Time Warping

Dynamic Time Warping (DTW) is an application of

the Dynamic Programming principles to the problem

of matching discrete time sequences. DTW was origi-

nally proposed for speech recognition applications

[12]. The goal of DTW is to find an elastic match

among samples of a pair of sequences X and Y that

minimizes a predefined distance measure. The algo-

rithm is described as follows. Lets define two

sequences

X x1; x2; :::; xi; :::; xIY y1; y2; :::; yj ; :::; yJ 1

and a distance measure as

di; j xi yj 2

between sequence samples. A warping path can be

defined as

C c1; c2; :::; ck; :::; cK 3where each ck represents a correspondence (i, j) be-

tween samples of X and Y . The initial condition of the

algorithm is set to

g1 g1; 1 d1; 1 w1 4where gk represents the accumulated distance after

k steps and w(k) is a weighting factor that must be

defined. For each iteration, gk is computed as

gk gi; j minck1 gk1 dck wk 5

until the Ith and Jth sample of both sequences respec-

tively is reached. The resulting normalized distance is

DX ;Y gKPKk1wk

6

where w(k) compensates the effect of the length ofthe sequences.

The weighting factors w(k) are defined in order to

restrict which correspondences among samples of both

sequences are allowed. In Fig. 1a, a common definition

of w(k) is depicted, and an example of a warping path

between two sequences is given. In this case, only three

transitions are allowed in the computation of gk. Con-

sequen tly, Eq. (5 ) becomes

gk gi; j mingi; j 1 di; jgi 1; j 1 2di; jgi 1; j di; j

24

35 7

which is one of the most common implementations

found in the literature. In Fig. 1b, an example of point

correspondences between two signatures is depicted to

visually show the results of the elastic alignment.

The algorithm has been further refined for signa-

ture verification by many authors [5, 7], reaching a

notable verification performance. For example, the

distance measure d(i, j) can be alternatively defined,

or other normalization techniques may be applied

to the accumulated distance gK among sequences.

DTW can be also applied independently for each

stroke, which may be specially well suited for oriental

signatures, since they are generally composed of seve-

ral strokes. Although the DTW algorithm has been

replaced in speech-related applications by more pow-

erful approaches such as HMMs, it remains as a highly

effective tool for signature verification as it is best

suited for small amounts of training data, which is

the common case in signature verification.

Hidden Markov Models

Hidden Markov Models (HMM) have been widely

used for speech recognition applications [13] as well

Signature Matching S 1193

S

as in many handwriting recognition applications.

Several approaches using HMMs for dynamic signa-

ture verification have been proposed in the last years

[8, 9, 10, 11]. An HMM represents a double stochas-

tic process, governed by an underlying Markov

chain, with a finite number of states and a random

function set that generate symbols or observations

each of which is associated with one state [11].

Observations in each state are modeled with GMMs

in most speech and handwriting recognition applica-

tions. In fact, GMMs can be considered a single-state

HMM and have also been successfully used for signa-

ture verification [14]. Given a sequence of multi-

dimensional vectors of observations O defined as

O o1; o2; . . . ; oN ;

corresponding to a given signature, the goal of HMM-

based signature matching is to find the probability that

this sequence has been produced by a Hidden Markov

Model M

POjM;

where M is the signature model computed during

enrollment.

The basic structure of an HMM using GMMs to

model observations is defined by the following elements:

Number of hidden states N. Number of Gaussian mixtures per state M. Probability transition matrix A {aij}, which con-

tains the probabilities of jumping from one state to

another or staying on the same state.

In Fig. 2, an example of a possible HMM con-

figuration is shown. Hidden Markov Models are

usually trained in two steps using the enrollment

signatures. First, state transition probabilities and

observation statistical models are estimated using

a Maximum Likelihood algorithm. After this, a re-

estimation step is carried out using the Baum-Welch

algorithm. The likelihood between a trained HMM

and an input sequence (i.e., the matching score) is

computed by using the Viterbi algorithm. In [10],

the Viterbi path (that is, the most probable state tran-

sition sequence) is also used as a similarity measure.

A detailed description of Hidden Markov Models is

given in [13].

Within HMM-based dynamic signature verifica-

tion, the existing approaches can be divided in regional

and local. In regional approaches, the extracted time

Signature Matching. Figure 1 (a) Optimal warping path between two sequences obtained with DTW. Point-to-point

distances are represented with different shades of gray, lighter shades representing shorter distances and darker

shades representing longer distances. (b) Example of point-to-point correspondences between two genuine

signatures obtained using DTW.

1194S Signature Matching

sequences are further segmented and converted into

a sequence of feature vectors or observations, each

one representing regional properties of the signature

signal [9, 11]. Some examples of segmentation bound-

aries are null vertical velocity points [9] or changes in

the quantized trajectory direction [11]. On the other

hand, local approaches directly use the time functions

as observation sequences for the signature modeling

[8, 10, 14].

Finding a reliable and robust model structure for

dynamic signature verification is not a trivial task.

While too simple HMMs may not allow to model

properly the user signatures, too complex models

may not be able to model future realizations due to

overfitting. On the other hand, as simple models have

less parameters to be estimated, their estimation may

be more robust than for complex models. Two main

parameters are commonly considered while selecting

an optimal model structure: the number of states and

the number of Gaussian mixtures per state [8]. Some

approaches consider a user-specific number of states

[10], proportional to the average signature duration or

a user-specific number of mixtures [14]. Most of the

proposed systems consider a left-ro-right configura-

tion without skips between states, also known as

Bakis topology (see Fig. 2).

Other Techniques

More examples of signature matching techniques in-

clude Neural Networks, in particular Bayesian,

multilayer, time-delay Neural Networks and radial-

basis functions among others have been applied for

signature matching. Other examples include Structural

approaches, which model signatures as a sequence, tree

or graph of symbols. Support Vector Machines have

also been applied for signature matching. The reader is

referred to [15] for an exhaustive list of references

related to these approaches.

Fusion of the feature- and function-based

approaches has been reported to provide better perfor-

mance than the individual systems [4].

Off-line Signature Matching

The proposed approaches for off-line signature match-

ing are notably heterogeneous compared to on-line

signature verification. These are mostly related to

image and shape recognition techniques and classical

statistical pattern recognition algorithms. They include

Neural Networks, Hidden Markov Models, Support

Vector Machines and distance-based classifiers among

others. A summary of off-line signature matching tech-

niques can be found in [15].

Related Entries

Off-line Signature Verification

On-line Signature Verification

Signature Features


Signature Matching. Figure 2 Graphical representation of a left-to-right N-state HMM, with M-component GMMs

representing observations and no skips between states.

Signature Matching S 1195

S

References

1. Nelson, W., Turin, W., Hastie, T.: Statistical methods for on-line

signature verification. Int. J. Pattern Recogn. Artif. Intell. 8(3),

749770 (1994)

2. Lee, L.L., Berger, T., Aviczer, E.: Reliable on-line human signa-

ture verification systems. IEEE Trans. Pattern Anal. Mach. Intell.

18(6), 643647 (1996)

3. Martinez-Diaz, M., Fierrez, J., Ortega-Garcia, J.: Universal Back-

ground Models for dynamic signature verification. In: Proceed-

ings IEEE Conference on Biometrics: Theory, Applications and

Systems, BTAS, pp. 16 (2007)

4. Fierrez-Aguilar, J., Nanni, L., Lopez-Penalba, J., Ortega-Garcia, J.,

Maltoni, D.: An on-line signature verification system based

on fusion of local and global information. In: Proceedings

of IAPR International Conference on Audio- and Video-Based

Biometric Person Authentication, AVBPA, Springer LNCS-3546,

pp. 523532 (2005)

5. Sato, Y., Kogure, K.: Online signature verification based on

shape, motion and writing pressure. In: Proceedings of sixth

International Conference on Pattern Recognition, pp. 823826

(1982)

6. Martens, R., Claesen, L.: Dynamic programming optimisation

for on-line signature verification. In: Proceedings fourth Inter-

national Conference on Document Analysis and Recognition,

ICDAR, vol. 2, pp. 653656 (1997)

7. Kholmatov, A., Yanikoglu, B.: Identity authentication using im-

proved online signature verification method. Pattern Recogn.

Lett. 26(15), 24002408 (2005)

8. Fierrez, J., Ramos-Castro, D., Ortega-Garcia, J., Gonzalez-

Rodriguez, J.: HMM-based on-line signature verification:

feature extraction and signature modeling. Pattern Recogn.

Lett. 28(16), 23252334 (2007)

9. Dolfing, J.G.A., Aarts, E.H.L., van Oosterhout, J.J.G.M.: On-line

signature verification with Hidden Markov Models. In: Proceed-

ings of the International Conference on Pattern Recognition,

ICPR, pp. 13091312. IEEE CS Press (1998)

10. Van, B.L., Garcia-Salicetti, S., Dorizzi, B.: On using the Viterbi

path along with HMM likelihood information for online signa-

ture verification. IEEE Trans. Syst. Man Cybern. B 37(5),

12371247 (2007)

11. Yang, L., Widjaja, B.K., Prasad, R.: Application of Hidden

Markov Models for signature verification. Pattern Recogn.

28(2), 161170 (1995)

12. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimi-

zation for spoken word recognition. IEEE Trans. Acoust. 26,

4349 (1978)

13. Rabiner, L.R.: A tutorial on Hidden Markov Models and selected

applications in speech recognition. Proceedings of the IEEE

77(2), 257286 (1989)

14. Richiardi, J., Drygajlo, A.: Gaussian Mixture Models for

on-line signature verification. In: Proceedings of ACM SIGMM

Workshop on Biometric Methods and Applications, WBMA.

pp. 115122 (2003)

15. Impedovo, D., Pirlo, G.: Automatic signature verification: The

state of the art. IEEE Trans. Syst. Man. Cybern. C Appl. Rev.

38(5), 609635 (2008)


OLAF HENNIGER1, DAIGO MURAMATSU2,

TAKASHI MATSUMOTO3, ISAO YOSHIMURA4,

MITSU YOSHIMURA5

1Fraunhofer Institute for Secure Information

Technology, Darmstadt, Germany2Seikei University, Musashino-shi, Tokyo, Japan3Waseda University, Shinjuku-ku, Tokyo, Japan4Tokyo University of Science, Shinjuku-ku, Tokyo,

Japan5Ritsumeikan University, Sakyo-ku, Kyoto, Japan

Synonyms

Handwritten signature recognition; signature/sign

recognition

Definition

A signature is a handwritten representation of name of

a person. Writing a signature is the established method

for authentication and for expressing deliberate deci-

sions of the signer in many areas of life, such as banking

or the conclusion of legal contracts. A related concept is

a handwritten personal sign depicting something else

than a persons name. As compared to text-independent

writer recognition methods, signature/sign recognition

goes with shorter handwriting probes, but requires to

write the same name or personal sign every time. Hand-

written signatures and personal signs belong to the

behavioral biometric characteristics as the person must

become active for signing.

Regarding the automated recognition by means of

handwritten signatures, there is a distinction between

on-line and off-line signature recognition. On-line sig-

nature data are captured using digitizing pen tablets,

pen displays, touch screens, or special pens and include

information about the pen movement over time (at

least the coordinates of the pen tip and possibly also the

pen-tip pressure or pen orientation angles over time).

In this way, on-line signature data represent the way a

signature is written, which is also referred to as signa-

ture dynamics. By contrast, off-line (or static) signa-

tures are captured as grey-scale images using devices

such as image scanners and lack temporal information.

1196S Signature Recognition

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