Automatic Biometric Signature Verification in Real Time

Automated Dynamic Verification of Behavioral Biometrics

• SIGNificant captures the behavioral biometric data of ones handwritten signature (including speed, acceleration, rhythm, movements in the air and pressure) and embed the signature into an electronic document.
• The captured biometric signature data is automatically verified dynamically against signature specimen stored encrypted in the systems database.
• The result of the automated signature verification is provided via a signed response ensuring its authenticity. Additionally a server-side audit trail keeps track of every action allowing statistical analysis on historical data or to verify a specific verification result.
• Documents may only get signed if the authentication based on ones handwritten signauture is passed succesfully

Signatures Are The Only Biometrics That Vary Over Time

• Every biometric system, such as an iris scan or fingerprints, may be hacked. Once hacked, the information may be used again and again because eyes and fingerprints do not change - they are static. 
• By contrast, a signature, even if hacked, is not reusable since no-one can ever sign the same signature twice; signatures are bound to be different from one another. 
• Also, the user can always change a signature and create a new personal profile.

How is SIGNificant Different from Other Signature Verification Solutions?

• xyzmo offers the world’s most complete, open and accurate real-time signature verification solution. The major advantage over other solutions on the market today is that our technology is able to compare a signature against a profile that is self-learning over time. Only this approach guarantees appropriate results for signature verification and authentication, because it is human nature never to sign twice in exactly the same way, and also to alter the signature constantly over a life-time.
• A comparison with only one sample signature is mathematically much easier to handle, and thus some companies offer solutions that are essentially inaccurate. These “low level” solutions merely pick one random signature as a basis for the comparison. This approach only works for people who always sign in exactly the same way. Most human beings do not behave like that, and thus this approach is simply not feasible for a broader usage of that technology. Ask 10 people to sign 6 times in a row, on a blank sheet of paper, and you can see for yourself how different most of thoese signatures will be.
• A more sophisticated but still unsatisfactory approach is to build a solution that takes several signatures – a profile – into consideration at the time of real-time comparison, but still uses a static profile that is not self-learning. This will deliver, better results than a comparison with just one signature at the start, but the comparison will get less and less accurate over time. This will happen for nearly 100% of human subjects.
• Only the xyzmo SIGNificant approach – based on self-learning profiles – provides sufficient accuracy in the long run. The SIGNificant Biometric Server takes the ability of self-learning profiles one step further, by using a sophisticated algorithm when the signature profile is being built, that assesses whether the profile is of sufficient quality or not. When people sign on a signature pad for the first time, they change their signing behavior a little bit; later they adjust it after they get used to this new technology, or the new way of signing on a signature tablet, until it becomes the “usual” way. Thus it is business-critical to take more than two or three samples for each profile and, on top of that, to check by intelligent algorithms to see if these profiles are a robust base for later verification. It is much better to reject improper signatures from a profile at the time the profile is being create, and ask a customer to sign one additional time,than to generate a poor profile and use it for later comparison.