On the use of first and second derivative approximations for biometric online signature recognition
This work addresses incremental improvements in feature extraction for biometric security systems, specifically for online signature recognition.
The paper tackled the problem of improving biometric online signature recognition by comparing first and second derivative approximation methods, finding that an 11-point approximation outperformed a 1-point approximation with a 1.4% increase in identification rate, 36.8% reduction in random forgeries, and 2.4% reduction in skilled forgeries.
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