Online signature verification using signature down-sampling and signer-dependent sampling frequency

Abstract

Online signature verification considers signatures as time sequences of different measurements of the signing instrument. These signals are captured on digital devices and therefore consist of a discrete number of samples. To enrich or simplify this information, several verifiers employ resampling and interpolation as a preprocessing step to improve their results; however, their design decisions may be difficult to generalize. This study investigates the direct effect of the sampling rate of the input signals on the accuracy of online signature verification systems without using interpolation techniques and proposes a novel online signature verification system based on a signer-dependent sampling frequency. Twenty verifier configurations were created for five different public signature databases and a variety of popular preprocessing approaches and evaluated for 20–40 different sampling rates. Our results show that there is an optimal range for the sampling frequency and the number of sample points that minimizes the error rate of a verifier. A sampling frequency range of 15–50 Hz and a signature point count of 60–240 provided the best accuracies in our experiments. As expected, lower ranges showed inaccurate results; interestingly, however, higher frequencies often decreased the verification accuracy. The results show that one can achieve better or at least the same verification accuracies faster by down-sampling the online signatures before further processing. The proposed system achieved competitive results to state-of-the-art systems for different databases by using the optimal sampling frequency. We also studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The proposed system was tested using 500 different verification methods and improved the accuracy in 92% of the test cases compared to the usage of the original frequency.

Dynamic Signature Verification Using Pattern Recognition

“…The sampling frequency range is determined by specific vectors and functions, offering pertinent information for signature verification and decreasing computation time. Experimental outcomes indicate the method's success in achieving high accuracy in both verification and prediction, thereby enhancing the efficiency of biometric systems [9].…”

Offline Handwritten Signature Identification based on Hybrid Features and Proposed Deep Model

“…Template-based approaches compare a known template of the signature with a template of an unidentified signature. Machine learning-based approaches use neural networks to learn the characteristics of the signature and then recognise it using the discovered patterns [8].…”

Research on Authentic Signature Identification Method Integrating Dynamic and Static Features

“…The features extracted from offline images can be combined to form a variety of effective features with uniqueness that cannot be ignored. Online signatures are obtained by signing on touch screen devices, such as tablets and cell phones, and many features are obtained by using a special pen and tablet and a scanned signature image [7]. Online handwriting recognition can be performed by collecting rich information, such as writing speed, angle, strength used by writers and stroke order online [8].…”