Cover Image

Study on the effect of number of training samples on HMM based offline and online signature verification systems

This paper reports on the effect that the number of samples used in training a signature verification system has on the system's accuracy which is describes by the pair combination of the system False Acceptance Rate (FAR) and False Rejection Rate (FRR). This paper also describes such an effect...

Full description

Saved in:
Bibliographic Details
Main Authors: Ahmad S.M.S., Shakil A., Balbed M.A.M.
Other Authors: 24721182400
Format: Conference paper
Published: 2023
Subjects:
Biometrics
Hidden Markov models
Information technology
Markov processes
Paper
Speech recognition
Systems engineering
Capture rates
False acceptance rates
False rejection rates
Malaysians
Offline
Offline signature verifications
Online signature verification systems
Signature verifications
System failures
Training samples
Online systems
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper reports on the effect that the number of samples used in training a signature verification system has on the system's accuracy which is describes by the pair combination of the system False Acceptance Rate (FAR) and False Rejection Rate (FRR). This paper also describes such an effect on the system Failure to Capture Rate (FCR), which is often neglected by biometrics study. The paper covers both online and offline signature verification systems developed using Hidden Markov Models (HMMs). Experimental results are based on Sigma - a database of Malaysian signatures with over 6,000 genuine samples and 2,000 skilled forgeries collected in a real life environment. � 2008 IEEE.

Similar Items