Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: On-line signature verification still remains a challenging task within biometrics. Due to their behavioural nature (opposed to anatomic biometric traits), signatures present a notable variability even beten successive realizations. This leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the two modalities,  propose a method for the generation of enhanced synthetic static samples from on-line data. Such synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of the exactly same signatures. Different findings and conclusions are drawn regarding the discriminative por of on-line and off-line signatures and of their potential combination both in the random and skilled impostors scenarios. HighlightsNovel generation method of dynamically enhanced synthetic off-line signatures.Novel on-line signature verification architecture.Findings on the fusion of on-line and off-line signature.Findings on the differences beten the random and skilled forgeries scenarios.Fully reproducible protocol carried out on a large public benchmark.
['Javier Galbally', 'Moises Diaz-Cabrera', 'Miguel A. Ferrer', 'Marta Gomez-Barrero', 'Aythami Morales', 'Julian Fierrez']
I am writing a research paper on a idea On-line signature recognition through the combination of real dynamic data and synthetically generated static data
[{"from": "human", "value": "I am writing a research paper on a idea On-line signature recognition through the combination of real dynamic data and synthetically generated static data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: On-line signature verification still remains a challenging task within biometrics. Due to their behavioural nature (opposed to anatomic biometric traits), signatures present a notable variability even beten successive realizations. This leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the two modalities,  propose a method for the generation of enhanced synthetic static samples from on-line data. Such synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of the exactly same signatures. Different findings and conclusions are drawn regarding the discriminative por of on-line and off-line signatures and of their potential combination both in the random and skilled impostors scenarios. HighlightsNovel generation method of dynamically enhanced synthetic off-line signatures.Novel on-line signature verification architecture.Findings on the fusion of on-line and off-line signature.Findings on the differences beten the random and skilled forgeries scenarios.Fully reproducible protocol carried out on a large public benchmark."}]