Comparison of Static Signature Identification using Artificial Neural Networks Based on Haar, Daubechies and Symlets Wavelet Transformations

Abstract

Signature is a biometric attribute that is quite important for each individual that can be used as self-identity. Until now, the signature is still used as a sign of legal approval and is agreed upon by everyone. This makes the signature worthy of attention from a security aspect. Various approaches have been proposed in the development of signature identification to minimize signature forgery. This study will discuss the identification of signatures by using the image of the signature on paper. This identification consists of two processes, namely training and testing by utilizing Artificial Neural Networks Backpropagation and Wavelet Transform. Optimal results are obtained by using ANN which has learning rate 0,09, two hidden layers, each 20 and 10 nodes with the most superior Wavelet Haar reaching 94.44%.