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

• 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

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.