On-line signature verification using LPC cepstrum and neural networks

Authors:
Quen-Zong Wu;I-Chang Jou;Suh-Yin Lee
Affiliations:
Dept. of Comput. Sci. & Inf. Eng., Nat. Chiao Tung Univ., Hsinchu;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
1997

Citing 0
Cited 10

On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Signature Verification by Neural Networks with Selective Attention

Applied Intelligence
Gaussian Mixture Models for on-line signature verification

WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Linear predictive coding and cepstrum coefficients for mining time variant information from software repositories

MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Discriminant Substrokes for Online Handwriting Recognition

ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A writer identification system for on-line whiteboard data

Pattern Recognition
A hybrid ANN-based technique for signature verification

CI'10 Proceedings of the 4th WSEAS international conference on Computational intelligence
A new online signature verification system based on combining Mellin transform, MFCC and neural network

Digital Signal Processing
An efficient online signature verification scheme using dynamic programming of string matching

ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Wireless sensor network system for supporting nursing context-awareness

International Journal of Autonomous and Adaptive Communications Systems

Quantified Score

Hi-index	0.00

Visualization

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%