| dc.description.abstract | Signature Verification System has been widely used in several occasions that people need to provide their identities, including in government institution and in some business activities. Using handwritten signature as a biometric verification has lots of advantages, unlike passwords, passwords could be forgotten or embezzled. Also, it has lower cost than traditional biometric verification, such as iris, face, and finger print, which need expensive hardware equipment to capture images information. We can acquire handwritten signature just through a digitizer or a tablet, which is less expensive. However, there are still some drawbacks exist in handwritten signature. The changes in the characteristics of handwritten signature from the exact same person could be huge. Also, it is easier to be forged compared with other traditional biometric verification.
In this thesis, Leap Motion somatosensory device is used to detect the biometrics of users when signing their names, such as velocity, angular velocity, degree of force, etc. The device then divides the signature pattern into two parts, the shape of characters, Real Stroke, and the way individual uses its pen, Virtual Strokes. Those two parts are the basis of biometrics. According to the biometrics that real strokes or virtual strokes contain, it will produce different combination of features. The features are processed by PCA which promote the ability of identification. In the experiment, this paper aims at the biometrics produced from different ways of using pen, utilizing Back Propagation Neural Network, BPN, to train for sig-nature samples from different individuals and uses the model in identification. This paper will discuss the effect of identification under different parameters in BPN. The result indicates that applying different ways of using pen and detecting biometrics, both of which are mentioned in the paper, on signature confirmation system has better effect on identification, hence enhancing the security of information. | en_US |