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Handwritten Signatures Verification Based on Arm and Hand Muscles Synergy Publisher



Asemi A1 ; Maghooli K2 ; Rahatabad FN2 ; Azadeh H3
Authors
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Authors Affiliations
  1. 1. Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
  2. 2. Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
  3. 3. Department of Occupational Therapy, School of Rehabilitation Sciences, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Biomedical Signal Processing and Control Published:2022


Abstract

Intra-personal variability, which measures the difference between people's signatures, may be affected by various signing challenges. Considering the variety of physical conditions, it is almost impossible for persons to write their exact handwritten signature in the same way in several attempts. Also, the authentication system requires less complexity to respond quickly to real-time applications. This study attempts to confirm handwritten signatures by using hand muscle synergy as a biometric characteristic. To design the signature verification system, surface electromyography (EMG) signals from eight (arm and hand) muscles of the volunteers were recorded by surface EMG pads during the signing. Muscle synergy was extracted from EMG signals after preprocessing using the non-negative matrix factorization (NMF) method. Genuine and forgery data are then classified by the K-means classifier. The system achieves an equal error rate (EER) of 2.75 to identify the extracted data related to the genuine and forged signatures. © 2022
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1. The Effect of Individual Stress on the Signature Verification System Using Muscle Synergy, Biomedical Signal Processing and Control (2024)
2. Survey of the Stability of Uniqueness of Muscle Synergy Patterns in Handwritten Signature Over Time, Majlesi Journal of Electrical Engineering (2023)
3. Outlier Detection in High-Density Surface Electromyographic Signals, Medical and Biological Engineering and Computing (2012)
4. A Real-Time Method for Decoding the Neural Drive to Muscles Using Single-Channel Intra-Muscular Emg Recordings, International Journal of Neural Systems (2017)
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