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In other words, the use of a signature can do both, proliferated by the legislation enacted by the Clinton Administration which makes signatures even legally binding in e-commerce transactions. The most common form of signature with which we are the most familiar with are the type when we sign our name at the Point of Sale terminal at our local stores.

For instance, when you sign your swipe your credit card through the Point of Sale system, you are prompted to sign your name. The image of your signature then gets transmitted to the sensor pad, and from there, to the credit card database where specialized software then matches the signature which is stored in the central databases to what has just been presented.

If the match is authentic, the transaction is then subsequently approved, and the customer can then procure their products.

But, there is often confusion between this type of signature and a “digital signature”, as well as a major difference. For example, digital signatures are not based upon the actual, physical signature itself.

Rather, it is based upon encrypted bit strings which are attached to electronic documents, which can be used to authenticate and approve all types and kinds electronic transactions, and to legally approve and bind “paperless documents” and records.

The Differences Between a Signature & Signature Recognition

And in this same regard, there is also confusion between the actual, physical signature just described, and the signature recognition itself. With signature recognition, it is very important to note that it is not the image of the actual physical signature which is stored and compared in the signature recognition database.

Rather, it is the behavioral patterns inherent to the process of signing which is stored and compared. This includes the changes in timing, pressure, and speed in which the signature is composed. Although it is comparatively straightforward to duplicate the visual appearance of a signature, it is very difficult to duplicate behavioral characteristics. This process is known as “dynamic signature recognition”.

Signature Recognition-How It Works

It should be noted at this point that signature recognition is primarily used for verification purposes only. While in the most theoretical sense it could potentially be used for identification applications, in the real world, it would prove to be very impractical.

This is so for a number of reasons. First, the hand can be greatly affected by genetic factors, various types and kinds of physical features (such as ailments, and the aging process of the hand we all experience at some point in time or another). Second, the signature is very dynamic, and can change very quickly over time, whether the individual has intentions to or not in altering their own signature. Third, unlike the other biometric technologies, there is virtually no permanence or long term stability (unlike the iris or the retina) associated with signature recognition, because of its ever changing nature.

The very first signature recognition devices utilized static variables such as the height, spacing, slope, as well as the various characteristics in terms of the shaping of the letters which are found in the signature. By the mid 1970’s, signature recognition became much more dynamic in the sense that various spatial, pressure, and temporal variables were taken into consideration. These variables included such factors as the downward pressure which was applied to the pen, the level of pressure at which the pen itself is gripped at, and the angle at which the pen is held while the individual signs his or her name, and even the time it takes for the signature to be completed.

Today’s signature recognition devices can now collect and analyze such variables as speed, acceleration, pauses, and the changes in pressure in which the individual signs their name on the special writing tablet. Neural network technology can also be incorporated with signature recognition which can literally learn the ever so slightest changes and variations in the way an individual signs their name over a pre established period of time, and make the necessary changes to the database.

Signature Recognition technology involves the use of a pen and a special writing tablet, which are connected to a local or central computer for processing and verification. To acquire the signature data during the enrolment process, an individual is required to sign his or her name several times on the writing tablet. It should be noted that the robustness of the Signature Recognition enrolment template is a direct function of the quality of the writing tablet.

A high-quality tablet will capture all of the behavioral variables (timing, pressure, and speed). In contrast, a low-spec tablet may not be able to capture all these variables. There are several constraints to the data acquisition phase. First, a signature cannot be too long or too short. If a signature is too long, too much behavioral data will be presented. As a result, it will be difficult for the Signature Recognition system to identify consistent and unique data points.

If a signature is too short, insufficient data will be captured, giving rise to a higher False Accept Rate. Second, the individual must complete the overall enrolment and verification process in the same type of environment and under the same conditions.

For example, if the individual stands during enrolment, but sits down during verification, the enrolment and verification templates may vary substantially (this is attributable to the amount of support given to the arm). Once the data acquisition phase has been completed, the signature recognition system extracts unique features from the behavioral characteristics, which includes the time needed to place a signature, the pressure applied by the pen to the writing tablet, the speed with which the signature is placed, the overall size of the signature, and the quantity as well as direction of the signature strokes.

With signature recognition templates, different values or ‘weights’ are assigned to each unique feature. These templates are therefore as small as 3kB. One of the biggest challenges in signature recognition is the constant variability in the signatures themselves. This is primarily due to the fact that an individual never signs their signature in the same fashion any given two, successive times.

For example, the writing slope can switch tangentially left to right (and vice versa), and up and down (and also vice versa); the exact pressure put on the pen can change greatly each and every time the individual has to submit a verification template; and even a reflective light on the surface of the signature recognition capture device can indirectly cause variances in the speed as well as the timing of the signature.

Signature Recognition-The Advantages & The Disadvantages

The most significant benefit of Signature Recognition is that it is highly resistant to impostors. For example, while it is quite easy to forge a signature, it is very difficult to ‘mimic’ the behavioral patterns inherent to the process of signing. Signature Recognition is well suited to high-value transactions. For example, Signature Recognition could be used to positively verify the business representatives involved in a transaction before any classified documents are opened and signed.

Second, compared to the other biometric technologies, Signature Recognition is deemed to be non- invasive. As a result, the technology could be widely accepted by users. We have all used our signature to authorize transactions, which does not in any way impede on our sense of privacy (or privacy-related rights).

Third, there is always a concern among users of biometric systems that templates may be stolen if the system is compromised, and that stolen templates cannot be replaced and/or changed. While this may be true for physiological biometrics (it would, for example, be very difficult to change the structure of your fingerprint or iris). Signature Recognition allows the behavioral dynamics of the way you place your signature to be changed, thus easing user concerns.

On the downside, Signature Recognition is prone to higher error rates, particularly when the behavioral characteristics of signatures are mutually inconsistent. In addition, users may have difficulties getting used to signature tablets, which could also result in higher error rates.

Signature recognition can also be compared against the seven criterion, with the end results being that the number of advantages it offers balances out for the most part, with its disadvantages:

  1. Universality: Probably its biggest strength, signature recognition can be used in almost any type and kind of language, ranging all the way to English to the most obscure languages ever heard of;
  2. Uniqueness: Obviously the more characteristics a signature has, the more unique it becomes. As mentioned, when the variables of speed, velocity, timing and pressure are introduced, dynamic signatures can virtually never be lost or stolen;
  3. Permanence: This is probably one of the greatest weaknesses of signature recognition. An individual’s signature can vary greatly over just a span of a few minutes, by such variables as fatigue, illness, stress, or distraction. These variables in turn, can also affect the ability of the individual to actually grip the writing pen used to create the dynamic signature, and exert the downward pressure which is needed;
  4. Collectability: The ability to collect raw signature samples is a direct correlation of the quality of the signature recognition system itself (meaning, a lower grade signature system will make it much more difficult to collect a robust signature sample);
  5. Performance: It has been recognized that the best performing signature recognition devices are about 96% accurate. But, the better the signature recognition device is, so will the cost. A strong advantage here is that signature recognition requires no end user training;
  6. Acceptability: In general, when compared to the other biometric technologies, most people are very comfortable with providing their signature, primarily because of its ease of use and non privacy rights issues. It is also important to note that the theft of personal data is much likely lesser with dynamic recognition technology versus graphic recognition technology (such as storing a fingerprint or an iris);
  7. Resistance to circumvention: This is one of signature recognition’s other biggest advantages: a signature which is dynamically produced is almost impossible to forge.

In summary, out of all biometric technologies, whether physical or behavioral, signature recognition offers most potential in terms of adaptability and implementation. This holds true from a number of perspectives.

First, there is its ease of use the user simply places his/her signature as he/she would normally do. There is, therefore, no need for end-user training, as is the case with other types of biometric technology, including Facial Recognition and Retinal Recognition. The training needed for the person operating the Signature Recognition device is also minimal.

Second, the implementation cost of Signature Recognition is low – the system consists of a special pen, a tablet, and software (which, in the case of retailers, could be installed in a POS terminal). Costs are minimal compared to those of much more complicated systems, such as a Retinal Scanning Device.

Third, a Signature Recognition system can easily be embedded in an organisation’s prevailing security processes, without excessively disrupting or affecting existing operations. To give an example, no major wiring or installation is needed (as is the case with a hand geometry scanner or fingerprint scanner). Signature Recognition could therefore prove a highly valuable tool for multi modal security solutions.