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Online Handwritten Signature Verification Method Based on Uni-Feature Correlation Coefficient between Signatures
1School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China.
Sensors (Basel, Switzerland)
|December 9, 2023
Summary
This study introduces a novel dynamic signature verification method using correlation analysis of uni-features, improving accuracy. Pen pressure achieved the highest verification performance at 93.46% on the SVC 2004 dataset.
Area of Science:
- Biometric recognition
- Pattern recognition
- Computer security
Background:
- Online handwritten signature verification is vital for security.
- Multi-feature fusion is common, but uni-feature contributions are understudied.
- Existing methods struggle with feature vector length and accuracy.
Purpose of the Study:
- To address challenges in uni-feature based dynamic signature verification.
- To propose a novel method using correlation analysis of uni-features.
- To investigate the performance and contribution of individual uni-features.
Main Methods:
- Proposed a dynamic signature verification method based on uni-feature correlation coefficients.
- Developed an alignment method for unequal feature vector lengths.
- Calculated correlation coefficients using a determined formula and Gaussian density function model.
Main Results:
- The proposed correlation-based method improved dynamic signature verification performance.
- Pen pressure feature demonstrated the best individual performance.
- Achieved a highest accuracy of 93.46% on the SVC 2004 dataset.
Conclusions:
- Uni-feature correlation analysis is effective for dynamic signature verification.
- Pen pressure is a highly discriminative feature for signature verification.
- The method offers a valuable approach for future research in biometric recognition.
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