Dynamic Signature Recognition Based on Fisher Discriminant

Biometric technologies are the primary tools for certifying identity of individuals. But cost of sensing hardware plus degree of physical invasion required to obtain reasonable success are considered major drawbacks. Nevertheless, the signature is generally accepted as one means of identification. We present an approach on signature recognition using face recognition algorithms to obtain class descriptors and then use a simple classifier to recognize signatures. We also present an algorithm to store the writing direction of a signature, applying a linear transformation to encode this data as a gray scale pattern into the image. The signatures are processed applying Principal Components Analysis and Linear Discriminant Analysis creating descriptors that can be identified using a KNN classifier. Results revealed an accuracy performance rate of 97.47% under cross-validation over binary images and an improvement of 98.60% of accuracy by encoding simulated dynamic parameters. The encoding of real dynamic data boosted the performance rate from 90.21% to 94.70% showing that this technique can be a serious contender to other signature recognition methods.