The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform
Marcus Liwicki - One of the best experts on this subject based on the ideXlab platform.
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ICDAR2015 competition on signature verification and writer identification for on- and off-line skilled forgeries (SigWIcomp2015)
2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015Co-Authors: Muhammad Imran Malik, Michael Blumenstein, Sheraz Ahmed, Angelo Marcelli, Linda Alewijns, Marcus LiwickiAbstract:This paper presents the results of the ICDAR 2015 competition on signature verification and writer identification for on- and off-line skilled forgeries jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic Casework. Two modalities (signatures and handwritten text) are considered and training and evaluation data are collected and provided by FHEs and PR-researchers. Four tasks are defined for four different languages; Bengali off-line signature verification, Italian off-line signature verification, German on-line signature verification, and English handwritten text based writer identification. In total, 40 systems have participated in this competition. The participants of the signatures modality were motivated to report their results in Likelihood Ratios (LRs). This has made the systems even more interesting for application in forensic Casework. For evaluating the performance of the systems, we have used the forensically substantial Cost of Log Likelihood Ratios (Ĉllr) in the case of signatures, and the F-measure in the case of handwritten text.
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ICFHR 2012 Competition on Automatic Forensic Signature Verification (4NsigComp 2012)
2012 International Conference on Frontiers in Handwriting Recognition, 2012Co-Authors: Marcus Liwicki, Muhammad Imran Malik, Linda Alewijnse, Elisa Van Den Heuvel, Bryan FoundAbstract:This paper presents the results of the ICFHR2012 Competition on Automatic Forensic Signature Verification jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic Casework. A forensic like training set containing disguised signatures along with skilled forgeries and genuine signatures was provided to the participants. They were motivated to report the results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic Casework. For evaluation we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Ĉllr). The system having the best Minimum Cost of Log Likelihood Ratio ( Ĉllrmin) is declared winner. Various experiments both including and excluding disguised signatures from the test set are reported.
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Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)
2011 International Conference on Document Analysis and Recognition, 2011Co-Authors: Marcus Liwicki, Muhammad Imran Malik, Elisa Van Den Heuvel, Xiaohong Chen, Charles Berger, Reinoud Stoel, Michael Blumenstein, Bryan FoundAbstract:The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic Casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic Casework. In collaboration with the German Research Center for Artificial Intelligence we have organized a signature verification competition on datasets with two scripts (Dutch and Chinese) in which we asked to compare questioned signatures against a set of reference signatures. We have received 12 systems from 5 institutes and performed experiments on online and offline Dutch and Chinese signatures. For evaluation, we applied methods used by Forensic Handwriting Examiners (FHEs) to assess the value of the evidence, i.e., we took the likelihood ratios more into account than in previous competitions. The data set was quite challenging and the results are very interesting.
Elisa Van Den Heuvel - One of the best experts on this subject based on the ideXlab platform.
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ICFHR 2012 Competition on Automatic Forensic Signature Verification (4NsigComp 2012)
2012 International Conference on Frontiers in Handwriting Recognition, 2012Co-Authors: Marcus Liwicki, Muhammad Imran Malik, Linda Alewijnse, Elisa Van Den Heuvel, Bryan FoundAbstract:This paper presents the results of the ICFHR2012 Competition on Automatic Forensic Signature Verification jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic Casework. A forensic like training set containing disguised signatures along with skilled forgeries and genuine signatures was provided to the participants. They were motivated to report the results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic Casework. For evaluation we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Ĉllr). The system having the best Minimum Cost of Log Likelihood Ratio ( Ĉllrmin) is declared winner. Various experiments both including and excluding disguised signatures from the test set are reported.
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Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)
2011 International Conference on Document Analysis and Recognition, 2011Co-Authors: Marcus Liwicki, Muhammad Imran Malik, Elisa Van Den Heuvel, Xiaohong Chen, Charles Berger, Reinoud Stoel, Michael Blumenstein, Bryan FoundAbstract:The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic Casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic Casework. In collaboration with the German Research Center for Artificial Intelligence we have organized a signature verification competition on datasets with two scripts (Dutch and Chinese) in which we asked to compare questioned signatures against a set of reference signatures. We have received 12 systems from 5 institutes and performed experiments on online and offline Dutch and Chinese signatures. For evaluation, we applied methods used by Forensic Handwriting Examiners (FHEs) to assess the value of the evidence, i.e., we took the likelihood ratios more into account than in previous competitions. The data set was quite challenging and the results are very interesting.
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ICDAR 2009 Signature Verification Competition
2009 10th International Conference on Document Analysis and Recognition, 2009Co-Authors: V. L. Blankers, K. Y. Franke, Elisa Van Den Heuvel, L. G. VuurpijlAbstract:Recent results of forgery detection by implementing biometric signature verification methods are promising. At present, forensic signature verification in daily Casework is performed through visual examination by trained forensic handwriting experts, without reliance on computer-assisted methods. With this competition on on- and offline skilled forgery detection, our objective is to make a first step towards bridging the gap between automated biometric performances and expert-based visual comparisons. We intent to combine realistic forensic Casework with automated methods by testing systems on a forensic-like new dataset. The results achieved by the participating systems are promising: 2.85\% Equal Error Rate (EER) on the online data and 9.15\% on the offline data. From these results we indicate that automated methods might be able to support forensic handwriting experts (FHEs) to formulate the strength of evidence that needs to be reported in court in the future.
Bryan Found - One of the best experts on this subject based on the ideXlab platform.
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ICFHR 2012 Competition on Automatic Forensic Signature Verification (4NsigComp 2012)
2012 International Conference on Frontiers in Handwriting Recognition, 2012Co-Authors: Marcus Liwicki, Muhammad Imran Malik, Linda Alewijnse, Elisa Van Den Heuvel, Bryan FoundAbstract:This paper presents the results of the ICFHR2012 Competition on Automatic Forensic Signature Verification jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic Casework. A forensic like training set containing disguised signatures along with skilled forgeries and genuine signatures was provided to the participants. They were motivated to report the results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic Casework. For evaluation we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Ĉllr). The system having the best Minimum Cost of Log Likelihood Ratio ( Ĉllrmin) is declared winner. Various experiments both including and excluding disguised signatures from the test set are reported.
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Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)
2011 International Conference on Document Analysis and Recognition, 2011Co-Authors: Marcus Liwicki, Muhammad Imran Malik, Elisa Van Den Heuvel, Xiaohong Chen, Charles Berger, Reinoud Stoel, Michael Blumenstein, Bryan FoundAbstract:The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic Casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic Casework. In collaboration with the German Research Center for Artificial Intelligence we have organized a signature verification competition on datasets with two scripts (Dutch and Chinese) in which we asked to compare questioned signatures against a set of reference signatures. We have received 12 systems from 5 institutes and performed experiments on online and offline Dutch and Chinese signatures. For evaluation, we applied methods used by Forensic Handwriting Examiners (FHEs) to assess the value of the evidence, i.e., we took the likelihood ratios more into account than in previous competitions. The data set was quite challenging and the results are very interesting.
L. G. Vuurpijl - One of the best experts on this subject based on the ideXlab platform.
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ICDAR 2009 Signature Verification Competition
2009 10th International Conference on Document Analysis and Recognition, 2009Co-Authors: V. L. Blankers, K. Y. Franke, Elisa Van Den Heuvel, L. G. VuurpijlAbstract:Recent results of forgery detection by implementing biometric signature verification methods are promising. At present, forensic signature verification in daily Casework is performed through visual examination by trained forensic handwriting experts, without reliance on computer-assisted methods. With this competition on on- and offline skilled forgery detection, our objective is to make a first step towards bridging the gap between automated biometric performances and expert-based visual comparisons. We intent to combine realistic forensic Casework with automated methods by testing systems on a forensic-like new dataset. The results achieved by the participating systems are promising: 2.85\% Equal Error Rate (EER) on the online data and 9.15\% on the offline data. From these results we indicate that automated methods might be able to support forensic handwriting experts (FHEs) to formulate the strength of evidence that needs to be reported in court in the future.
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The ICDAR 2009 signature verification competition
Proceedings of the International Conference on Document Analysis and Recognition ICDAR, 2009Co-Authors: V. L. Blankers, C. E. Van Den Heuvel, K. Y. Franke, L. G. VuurpijlAbstract:Recent results of forgery detection by implementing biometric signature verification methods are promising. At present, forensic signature verification in daily Casework is performed through visual examination by trained forensic handwriting experts, without reliance on computer-assisted methods. With this competition on on- and offline skilled forgery detection, our objective is to make a first step towards bridging the gap between automated biometric performances and expert-based visual comparisons. We intent to combine realistic forensic Casework with automated methods by testing systems on a forensic-like new dataset. The results achieved by the participating systems are promising: 2.85% Equal Error Rate (EER) on the online data and 9.15% on the offline data. From these results we indicate that automated methods might be able to support forensic handwriting experts (FHEs) to formulate the strength of evidence that needs to be reported in court in the future.
Muhammad Imran Malik - One of the best experts on this subject based on the ideXlab platform.
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ICDAR2015 competition on signature verification and writer identification for on- and off-line skilled forgeries (SigWIcomp2015)
2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015Co-Authors: Muhammad Imran Malik, Michael Blumenstein, Sheraz Ahmed, Angelo Marcelli, Linda Alewijns, Marcus LiwickiAbstract:This paper presents the results of the ICDAR 2015 competition on signature verification and writer identification for on- and off-line skilled forgeries jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic Casework. Two modalities (signatures and handwritten text) are considered and training and evaluation data are collected and provided by FHEs and PR-researchers. Four tasks are defined for four different languages; Bengali off-line signature verification, Italian off-line signature verification, German on-line signature verification, and English handwritten text based writer identification. In total, 40 systems have participated in this competition. The participants of the signatures modality were motivated to report their results in Likelihood Ratios (LRs). This has made the systems even more interesting for application in forensic Casework. For evaluating the performance of the systems, we have used the forensically substantial Cost of Log Likelihood Ratios (Ĉllr) in the case of signatures, and the F-measure in the case of handwritten text.
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ICFHR 2012 Competition on Automatic Forensic Signature Verification (4NsigComp 2012)
2012 International Conference on Frontiers in Handwriting Recognition, 2012Co-Authors: Marcus Liwicki, Muhammad Imran Malik, Linda Alewijnse, Elisa Van Den Heuvel, Bryan FoundAbstract:This paper presents the results of the ICFHR2012 Competition on Automatic Forensic Signature Verification jointly organized by PR-researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic Casework. A forensic like training set containing disguised signatures along with skilled forgeries and genuine signatures was provided to the participants. They were motivated to report the results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic Casework. For evaluation we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Ĉllr). The system having the best Minimum Cost of Log Likelihood Ratio ( Ĉllrmin) is declared winner. Various experiments both including and excluding disguised signatures from the test set are reported.
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Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)
2011 International Conference on Document Analysis and Recognition, 2011Co-Authors: Marcus Liwicki, Muhammad Imran Malik, Elisa Van Den Heuvel, Xiaohong Chen, Charles Berger, Reinoud Stoel, Michael Blumenstein, Bryan FoundAbstract:The Netherlands Forensic Institute and the Institute for Forensic Science in Shanghai are in search of a signature verification system that can be implemented in forensic Casework and research to objectify results. We want to bridge the gap between recent technological developments and forensic Casework. In collaboration with the German Research Center for Artificial Intelligence we have organized a signature verification competition on datasets with two scripts (Dutch and Chinese) in which we asked to compare questioned signatures against a set of reference signatures. We have received 12 systems from 5 institutes and performed experiments on online and offline Dutch and Chinese signatures. For evaluation, we applied methods used by Forensic Handwriting Examiners (FHEs) to assess the value of the evidence, i.e., we took the likelihood ratios more into account than in previous competitions. The data set was quite challenging and the results are very interesting.