![]() 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 signatures are processed applying Principal Components Analysis and Linear Discriminant Analysis creating descriptors that can be identified using a KNN classifier. 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. We present an approach on signature recognition using face recognition algorithms to obtain class descriptors and then use a simple classifier to recognize signatures. Nevertheless, the signature is generally accepted as one means of identification. But cost of sensing hardware plus degree of physical invasion required to obtain reasonable success are considered major drawbacks. ![]() Biometric technologies are the primary tools for certifying identity of individuals.
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