On-line signature verification using LPC cepstrum and neural networks

Abstract

An on-line signature verification scheme based on linear prediction coding (LPC) cepstrum and neural networks is proposed. Cepstral coefficients derived from linear predictor coefficients of the writing trajectories are calculated as the features of the signatures. These coefficients are used as inputs to the neural networks. A number of single-output multilayer perceptrons (MLPs), as many as the number of words in the signature, are equipped for each registered person to verify the input signature. If the summation of output values of all MLPs is larger than the verification threshold, the input signature is regarded as a genuine signature; otherwise, the input signature is a forgery. Simulations show that this scheme can detect the genuineness of the input signatures from a test database with an error rate as low as 4%