Adaptation of Writer-Independent Systems for Offline Signature Verification
- 1 September 2012
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2012 International Conference on Frontiers in Handwriting Recognition
- p. 434-439
- https://doi.org/10.1109/icfhr.2012.175
Abstract
Although writer-independent offline signature verification (WI-SV) systems may provide a high level of accuracy, they are not secure due to the need to store user templates for authentication. Moreover, state-of-the-art writer-dependent (WD) and writer-independent (WI) systems provide enhanced accuracy through information fusion at either feature, score or decision levels, but they increase computational complexity. In this paper, a method for adapting WI-SV systems to different users is proposed, leading to secure and compact WD-SV systems. Feature representations embedded within WI classifiers are extracted and tuned to each enrolled user while building a user-specific classifier. Simulation results on the Brazilian signature database indicate that the proposed method yields WD classifiers that provide the same level of accuracy as that of the baseline WI classifiers (AER of about 5.38), while reducing complexity by about 99.5%.Keywords
This publication has 16 references indexed in Scilit:
- Dynamic selection of generative–discriminative ensembles for off-line signature verificationPattern Recognition, 2012
- Writer-independent off-line signature verification using surroundedness featurePattern Recognition Letters, 2012
- Improving performance of HMM-based off-line signature verification systems through a multi-hypothesis approachInternational Journal on Document Analysis and Recognition (IJDAR), 2009
- Automatic Signature Verification: The State of the ArtIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2008
- Biometrics: A Tool for Information SecurityIEEE Transactions on Information Forensics and Security, 2006
- An Off-Line Signature Verification Method Based on the Questioned Document Expert’s Approach and a Neural Network ClassifierPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Off-line signature verification using HMM for random, simple and skilled forgeriesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A Feature-Based Serial Approach to Classifier CombinationPattern Analysis and Applications, 2002
- On measuring the distance between histogramsPattern Recognition, 2002
- A neural network approach to off-line signature verification using directional PDFPattern Recognition, 1996