Title: On-line signature verification using LPC cepstrum and neural networks
Authors: Wu, QZ
Jou, IC
Lee, SY
資訊工程學系
Department of Computer Science
Issue Date: 1-Feb-1997
Abstract: In this paper, 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 (MLP's), 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 MLP's is larger than 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 our test database with an error rate as low as 4%.
URI: http://dx.doi.org/10.1109/3477.552197
http://hdl.handle.net/11536/744
ISSN: 1083-4419
DOI: 10.1109/3477.552197
Journal: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Volume: 27
Issue: 1
Begin Page: 148
End Page: 153
Appears in Collections:Articles


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  1. A1997WD89200017.pdf