# On the use of first and second derivative approximations for biometric online signature recognition

arXiv: https://arxiv.org/abs/2406.00512 · HTML: https://mlsanity.com/paper/2406.00512/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 1 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health](https://mlsanity.com/paper/2202.12760/) (2022)
- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [ICDAR 2021 Competition on On-Line Signature Verification](https://mlsanity.com/paper/2106.00739/) (2021)
- [Online handwriting, signature and touch dynamics: tasks and potential applications in the field of security and health](https://mlsanity.com/paper/2202.12693/) (2022)
- [Biometric security technology](https://mlsanity.com/paper/2202.11459/) (2022)

## Built on by (most cited first, 1 of 1)

- [On the analysis of saturated pressure to detect fatigue](https://mlsanity.com/paper/2411.04128/) (2024)

## Abstract (arXiv)

This paper investigates the impact of different approximation methods in feature extraction for pattern recognition applications, specifically focused on delta and delta-delta parameters. Using MCYT330 online signature data-base, our experiments show that 11-point approximation outperforms 1-point approximation, resulting in a 1.4% improvement in identification rate, 36.8% reduction in random forgeries and 2.4% reduction in skilled forgeries