Dynamic signature analysis using minimum spectral features

Presents a technique to classify signatures produced by the pressure exerted on the pen tip. Before the features are extracted, a low-pass filter is designed to remove frequencies greater that 50 Hz. A segmentation method using moving-average filtering and gradient calculation is used to divide the time series data into segments. The autoregressive (AR) coefficients are derived from each segment. From the coefficients, the power spectral density (PSD) is determined for every segment. A genetic algorithm (GA) is used to select the range of frequencies that contains the most important information. The selected range of frequencies is then fed into a multilayer perceptron (MLP) classifier with one hidden layer for verification. A database of 1,000 signatures is used for training and testing. The system is tested for genuine as well as forged signatures. The result obtained showed an average error rate of 3.09-3.33%