A Hybrid System for Signature Verification

Authors:
C. W. Omlin
Affiliations:
-
Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Year:
2000

Citing 0
Cited 3

Java-Based Internet Biometric Authentication System

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification

Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Securing electronic customer-signatures in legally binding business processes: a case study from the insurance industry

Journal of Theoretical and Applied Electronic Commerce Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Biometric authentication has become a popular research topic due to its wide applicability including the prevention of fraud in financial transactions. Handwritten signature verification, in contrast with other biometric based authentication methods such as fingerprint and retinal scanning, has the advantage that it is already widely used to endorse financial transactions. However, very little verification on these signatures is done today in practical scenarios. This paper reports on our ongoing research on automatic, on-line, handwritten signature verification. The hybrid system consists of a Kohonen self-organizing map, which find cluster centers in the training data and Hidden Markov Models, which are trained to model the dynamics of signatures. Our initial results are very promising: The system achieves a 0% false rejection rate and a 13% false acceptance rate.