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**On-line Handwritten Signature Verification**

**A White Paper from Vector Intelligence, Inc.: January, 2002**

**History**

The core of BioSig™, the On-line Handwritten Signature Verification
system marketed by Vector Intelligence, Inc. (VII) was developed by
Lucent Technologies' Bell Labs. The Bell Labs' project took more than
five years of extensive research and development. The size of the
research group varied at times from one to four researchers. The effort
resulted in several patents, as well as in a highly tested and tuned
verification engine code.

VII holds exclusive licenses from Lucent to the signature verification
technology, plus a non-exclusive license to technology that provides for
secure attachment of handwritten electronic signatures to electronic
documents. VII also holds an exclusive license to the signature
verification engine's source code developed by Bell Labs.

**Patents**

The two critical signature verification patents are licensed exclusively
to VII.

> ***US 5,828,772: Method and apparatus for parametric signature
> verification using global features and stroke-direction codes***
>
> Signature verification techniques developed by other companies make
> use of so-called "global" features describing general characteristic
> of a signature, such as total time to sign. This patent secures the
> use of a combination of both "global" features of a signature, and
> "local" signature curve matching, to verify the authenticity of the
> signer. This technique provides for higher verification accuracy than
> reliance on global features alone. The local component is described
> below under "Sophisticated Curve Matching Techniques."
>
> ***US 5,745,592: Method for detecting forgery in a traced signature by
> measuring an amount of jitter***
>
> This is the second patent controlled exclusively by VII, and focuses
> on a critical element of forgery detection in handwritten
> signatures---a forgery created by tracing a signature has much more
> "jitter" than the original. Jitter is caused by microvibrations of the
> muscles in the fingers which control the process of handwriting. When
> a person signs their own signature, these muscles are coordinated and
> operate fluidly. What Lucent found was that when a forger attempts to
> copy another\'s signature, these same finger muscles operate less
> fluidly and cause the phenomenon of jitter, which can be accurately
> measured by this patented technology.
>
> ***US 5,898,156: Validation stamps for electronic signatures***
>
> The final patent licensed from Lucent allows VII to engage not only in
> any signature verification business (as per patent 5,828,772), but
> also in the business of verifying the authenticity of electronic
> documents. The combination of these two technologies is critical for
> any document-oriented applications.
>
> This patent describes a method of verifying that a given handwritten
> electronic signature was intended for the given electronic document.
> The technology covered in the patent prevents such forms of deception
> as the attachment of an authentic signature from one document to a
> different document.
>
> The patent also thwarts any attempt to fraudulently re-generate a
> handwritten signature and attach it to another document by
> re-constructing the timing information in a signature from its
> geometric shape as sampled by a tablet digitizer. It is oftentimes
> important to display a signature shape on an unencrypted portion of a
> document to visually present the signature to a user. However, such an
> image can present a security threat. The patent covers the entire
> concept of modifying the geometric characteristics of a signature to
> hide the signature stroke pattern and speed while preserving the shape
> of the signature.

**Sophisticated Curve Matching Techniques**

While the patents cover the key technological bases, a working system
requires further perfection of the methodologies described in patents.
In particular, very sophisticated curve matching algorithms were
developed by Bell Labs to build upon the idea patented in US 5,828,772.
As human signatures display a certain amount of variability, it was
considered technically challenging to reliably match the proper
signature curves. The technical team at Bell Labs experimented with
several approaches to curve matching and settled on one which VII
believes to be the first curve matching technique reliable enough for
commercial deployment.

This new technique, embodied in BioSig™, solves the challenge by
generating a "stroke direction code" (or "SDC") from a signature. It
does so by subdividing the signature into a sequence of line segments,
which Lucent refers to as links, between discrete points along the
signature. These links are ordered according to the time-sequence in
which the corresponding portions of the signature were made. Each link
is assigned a stroke-direction value that depends upon the orientation
of that link. The SDC of a signature is the resulting sequence of
stroke-direction values.

**Sample Signatures Acquisition - Training Process**

BioSig™ requires several signature samples from a person to build a
signature model for that person. After a sample has been acquired, the
system runs several algorithms to extract parameters for the signature
model as well as a model of the curve. The process is called training,
or enrollment.

Creation of a model is a very fast process and takes well under a second
on current generation PCs in single-user mode. Memory requirements are
easily within the capabilities of all currently used PC configurations.

On a UNIX platform, "training" speed depends on the hardware
configuration of the system as well as on the number of processes
competing for system resources. The training process is CPU-intensive.
On a UNIX platform the software is implemented as MT-safe library to
allow for multiple training requests to be processed simultaneously. VII
does not foresee any system degradation in a UNIX environment as the
training process happens in realtime, not batch mode, so that computing
demands will not typically be concentrated within a specific period of
time.

For optimal performance, BioSig™ should be set to generate a model from
six signatures. A model may be built from a smaller sample, but
verification accuracy may suffer. A sample of more than six signatures
usually provides for little improvement over a sample of six.

The software is very robust as to the natural variations in human
signatures. However, if a person consistently uses two or more quite
different signatures, for example, one with a middle initial and one
without, or one with a maiden name and another without one, separate
parameter sets (virtual "signature cards") should be created for each
one. The training process will reject signatures if they are too
dissimilar.

The software is very robust as to the skew of a signing line.

**Verification Process**

The Verification Process compares a newly submitted signature to the
model. A verification score is calculated to determine how closely a
newly submitted signature matches the signature description stored in a
reference database.[^1]

Depending on the business need or on the amount of money involved in a
transaction, the system can be tuned as to false acceptance / false
rejection ratio. Although the tradeoff between false acceptance and
false rejection is not linear and has a point of overall best
performance, certain business conditions may dictate an emphasis on one
aspect over the other.

Slightly higher accuracy can be achieved if two signatures are required
for verification.

Verification can be accomplished either on the front-end machine or on a
central server. Due to security concerns, the front-end verification
methodology is better suited to relatively secure environments such as
in-store POS terminals.

Like the training process, the verification process is very fast and
takes a fraction of a second on current PCs in single-user mode. On a
UNIX platform, verification is implemented as MT-safe library.

**Verification Accuracy**

BioSig™ demonstrates a very high decree of accuracy. It also proves to
be very robust in taking into account variance in human handwritten
signatures while still distinguishing true signatures from forgeries.

The current version of the software provides for 1% of false rejection
rate with 1.3% false acceptance rate with two verification
signatures.[^2] For comparison, the best human signature expert has
about 33% false acceptance / false rejection rate.

**System Requirements - Input Devices**

The technology is a software solution, and works with any digitizing
tablet that can provide (x,y,t) coordinates where x and y are position
coordinates on the digitizer, and t is time. Time does not have to be
real time, but should only provide for a consistent presentation of the
speed of a pen on the tablet. Substantially all digitizing tablets
manufactured today conform to these requirements. These tablets can be
either full scale graphics tablets, or other devices such as PDAs (Palm
or Pocket PC) or even the small touch pads replicating mouse operations
found on most notebook computers.

The technology can also make use of the pressure component of a
signature if supported by underlying hardware. However, extensive tests
conducted at Bell Labs showed that addition of pressure to the model is
more likely to simply increase the noise factor and hence adversely
affect the overall false acceptance / false rejection ratio. This is due
to the fact that pressure ordinarily varies considerably depending on
the posture of the signer, weight of the pen, and other factors
unrelated o the identity of the signer.

For a MS Windows-based system front end, VII created a driver that
converts input from any Wintab-compatible digitizer into the format
required by the system for signature training and verification. Wintab
is the current industry standard, and practically all tablets
manufactured to work under MS Windows support it.

**System Requirements - Storage**

The system creates a model of a handwritten signature as a set of
parameters extracted from signature shapes and speeds in a training
sample. The size of the parameter set can be reduced to 100 bytes.

Models of the signatures can be stored either centrally in any
conventional database, or on any other device that has 100 bytes
available storage per model, such as a "smart card." A signature model
can even fit into the available free storage on a conventional credit
card's magnetic strip.

Due to the small size of the parameter set, there should be very little
impact on network traffic or on server load in sending a signature over
a network for any modern network configurations or server platforms.

If graphical representation of a signature is required for some business
purposes, that graphical representation usually takes an additional 3K
to 5K of storage space. VII also offers "SMP" technology, which stores
information about graphical input to a pen or stylus-based computer in a
highly-compressed, vector-based format, as a space-saving alternative to
bitmapped graphics. Call or write VII for additional information about
SMP.

**Security**

It is not possible to re-create a signature from its model. Thus,
storing only models of the signatures is usually advantageous from a
security point of view. However, if a graphical representation of a
signature is required by business considerations, this representation
can be stored together with the model.

Patent US 5,898,156 described above provides a security mechanism to
prevent the reconstruction of signature model from a visual
representation of a signature.

Standard database security mechanisms provided by every major database
vendor, plus properly implemented operations procedures, create
additional levels of security for the stored signatures.

Copyright 1999-2002, OpenSociety Technologies and Vector Intelligence,
Inc.

Proprietary and Confidential

[^1]: Verifying a trial signature involves comparing its feature values
    to the reference feature values. This comparison is conveniently
    carried out by computing a total global feature error. This error is
    obtained by combining (in an appropriate norm) the individual
    discrepancies between each trial-signature feature value and the
    corresponding reference value. The total error is compared to a
    threshold, which is usefully established with reference to deviation
    measures, such as the standard deviations, of the global features
    over the group of reference signatures. That is, a larger total
    error should be tolerated if the global features exhibit a high
    degree of scatter, than if they show a low degree of scatter.

    For verification purposes, an SDC error (which can be combined with
    the total global feature error) is readily computed by comparing the
    SDC of a trial signature with an average or representative SDC
    derived from the reference signatures. We refer to this average or
    representative SDC as the SDC template.

[^2]: Based on tests run against the Lucent Technologies signature
    database containing 550 genuine signatures and 325 forgeries from 58
    subjects. Tests with similar results were also conducted on NCR
    databases containing 1772 genuine signatures and 825 forgeries from
    145 subjects. However, the NCR database was not available for
    testing of the latest version of the software.