On the use of first and second derivative approximations for biometric online signature recognition
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910467805413376 |
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| author | Faundez-Zanuy, Marcos Diaz, Moises |
| author_facet | Faundez-Zanuy, Marcos Diaz, Moises |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00512 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the use of first and second derivative approximations for biometric online signature recognition Faundez-Zanuy, Marcos Diaz, Moises Computer Vision and Pattern Recognition 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 |
| title | On the use of first and second derivative approximations for biometric online signature recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.00512 |