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The present work analyzes performance, abilities and contributions of the human being (layman) in semi-automatic signature recognition systems. During the last decade the performance of Automatic Signature Verification systems have been improved based on new machine learning techniques and better knowledge about intraclass and interclass variability of signers. However, there is still room for improvements...
This work explores human intervention to improve Automatic Signature Verification (ASV). Significant efforts have been made in order to improve the performance of ASV algorithms over the last decades. This work analyzes how human actions can be used to complement automatic systems. Which actions to take and to what extent those actions can help state-of-the-art ASV systems is the final aim of this...
This work visualizes a state - of - the - art study of researches in the biometry specially in signature recognition, to show the potential of collaborative tools such as crowdsourcing and a tool for human - assisted schemes to improve automatic signature recognition systems. We present an analysis of experiments of evaluation of signatures made through crowdsourcing and labeling of attributes inspired...
This work explores the human ability to recognize the authenticity of signatures. We use crowdsourcing to analyze the different factors affecting the performance of humans without Forensic Document Examiner experience. We present different experiments according to different scenarios in which laymen, people without Forensic Document Examiner experience, provide similarity measures related with the...
This work explores crowdsourcing for the establishment of human baseline performance on signature recognition. We present five experiments according to three different scenarios in which laymen, people without Forensic Document Examiner experience, have to decide about the authenticity of a given signature. The scenarios include single comparisons between one genuine sample and one unlabeled sample...
This work explores human-assisted schemes for improving automatic signature recognition systems. We present a crowdsourcing experiment to establish the human baseline performance for signature recognition tasks and a novel attribute-based semi-automatic signature verification system inspired in FDE analysis. We present different experiments over a public database and a self-developed tool for the...
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