TY - EJOUR
AU - Asemi, Arsalan
AU - Asemi, Arsalan
AU - Rahatabad, Fereidoun Nowshiravan
AU - Azadeh, Hamid
PY - 2024
DA - February
TI - Survey of the Stability of Uniqueness of Muscle Synergy Patterns in Handwritten Signature over Time
T2 - Majlesi Journal of Electrical Engineering
VL - 17
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/survey-of-the-stability-of-uniqueness-of-muscle-synergy-patterns-in-handwritten-signature-over-time/
DO - 10.30486/mjee.2023.1973225.1009
N2 - Biometric characteristics of the human body can play a decisive role in the accuracy of automatic signature verification systems due to their stability over time and resistance to variability in different conditions. In this study, the accuracy of an automatic handwritten signature verification system is checked for nine months. In this system, the electromyography (EMG) signals from the hand muscles of people during signing are recorded at different times up to nine months, and after the pre-processing of the signals, muscle synergy patterns are extracted by the non-negative matrix factorization (NMF) method. Finally, the patterns extracted by the SVM classifier are classified into two classes: genuine and forgery signatures.
IS - 3
PB - OICC Press
KW - Handwritten Signature, EMG, Muscle Synergy 
EN -