On-line signature verification system using probabilistic feature modelling

Signature verification is a popular biometric authentication technique. Although the use of other biometrics like retinal patterns and fingerprints are quite prevalent, they are not simple methods. With the development of very handy digitizer tablets, signatures can be captured on-line and can be processed for verification. Since signatures of the same person can vary with time and state of mind, it is necessary to develop an efficient signature verification system. We propose a signature verification system which extracts certain dynamic features derived from velocity and acceleration of the pen together with other global parameters like total time taken and number of pen-ups. The features are modeled by fitting probability density functions, i.e., by estimating the mean and variance, which could probably take care of the variations of the features of the signatures of the same person with respect to time and state of mind