Investigating the common authorship of signatures by off-line automatic signature verification without the use of reference signatures
Investigating the common authorship of signatures by off-line automatic signature verification without the use of reference signatures
In automatic signature verification, questioned specimens are usually compared with reference signatures. In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model while a writer-independent system requires a set of reference signatures from several signers to develop the model of the system. This paper addresses the problem of automatic signature verification when no reference signatures are available. The scenario we explore consists of a set of signatures, which could be signed by the same author or by multiple signers. As such, we discuss three methods which estimate automatically the common authorship of a set of off-line signatures. The first method develops a score similarity matrix, worked out with the assistance of duplicated signatures; the second uses a feature-distance matrix for each pair of signatures; and the last method introduces pre-classification based on the complexity of each signature. Publicly available signatures were used in the experiments, which gave encouraging results. As a baseline for the performance obtained by our approaches, we carried out a visual Turing Test where forensic and non-forensic human volunteers, carrying out the same task, performed less well than the automatic schemes.
Terms--Off-line signature verification, biometrics, no reference signatures, feature-distance matrix, signature complexity
487-499
Diaz, Moises
58fb23f4-d690-4442-aa50-317b74b2e286
Ferrer, Miguel A.
5bf00f64-9429-46bf-a9cf-92615ad403a6
Ramalingam, Soodamani
f64594e4-af79-434a-bb1e-bf6726d7c59c
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
20 June 2019
Diaz, Moises
58fb23f4-d690-4442-aa50-317b74b2e286
Ferrer, Miguel A.
5bf00f64-9429-46bf-a9cf-92615ad403a6
Ramalingam, Soodamani
f64594e4-af79-434a-bb1e-bf6726d7c59c
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Diaz, Moises, Ferrer, Miguel A., Ramalingam, Soodamani and Guest, Richard
(2019)
Investigating the common authorship of signatures by off-line automatic signature verification without the use of reference signatures.
IEEE Transactions on Information Forensics Security, 15, .
(doi:10.1109/TIFS.2019.2924195).
Abstract
In automatic signature verification, questioned specimens are usually compared with reference signatures. In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model while a writer-independent system requires a set of reference signatures from several signers to develop the model of the system. This paper addresses the problem of automatic signature verification when no reference signatures are available. The scenario we explore consists of a set of signatures, which could be signed by the same author or by multiple signers. As such, we discuss three methods which estimate automatically the common authorship of a set of off-line signatures. The first method develops a score similarity matrix, worked out with the assistance of duplicated signatures; the second uses a feature-distance matrix for each pair of signatures; and the last method introduces pre-classification based on the complexity of each signature. Publicly available signatures were used in the experiments, which gave encouraging results. As a baseline for the performance obtained by our approaches, we carried out a visual Turing Test where forensic and non-forensic human volunteers, carrying out the same task, performed less well than the automatic schemes.
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Published date: 20 June 2019
Keywords:
Terms--Off-line signature verification, biometrics, no reference signatures, feature-distance matrix, signature complexity
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Local EPrints ID: 489469
URI: http://eprints.soton.ac.uk/id/eprint/489469
PURE UUID: 79d9a200-91d2-4706-a0a6-dc8e9dd21c8f
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Date deposited: 25 Apr 2024 16:30
Last modified: 28 Apr 2024 02:05
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Contributors
Author:
Moises Diaz
Author:
Miguel A. Ferrer
Author:
Soodamani Ramalingam
Author:
Richard Guest
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