On-line signature verification with hidden Markov models

J.G.A. Dolfing; E.H.L. Aarts; J.J.G.M. Oosterhout, van

doi:10.1109/ICPR.1998.711942

On-line signature verification with hidden Markov models

J.G.A. Dolfing, E.H.L. Aarts, J.J.G.M. Oosterhout, van

Algorithms, Geometry and Applications

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

Samenvatting

This paper addresses the problem of online signature verification based on hidden Markov models (HMM). We use a novel type of digitizer tablet and pay special attention to the use of pen-tilt. We investigate the verification reliability based on different forgery types. We compare the discriminative value of the different features based on a linear discriminant analysis (LDA) and show that pen-tilt is important. On the basis of home-improved, over-the-shoulder and professional forgeries, we show that the amount of dynamic information available to an imposter is important and that forgeries based on paper copies are easier to detect. The results obtained with a database of almost 5000 signatures of 51 persons with highly skilled forgeries include equal-error rates between 1% and 1.9%.

Originele taal-2	Engels
Titel	14th International Conference on Pattern Recognition (Brisbane, Queensland, Australia, August 16-20, 1998)
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	1309-1312
ISBN van geprinte versie	0-8186-8512-3
DOI's	https://doi.org/10.1109/ICPR.1998.711942
Status	Gepubliceerd - 1998

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10.1109/ICPR.1998.711942

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Citeer dit

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title = "On-line signature verification with hidden Markov models",

abstract = "This paper addresses the problem of online signature verification based on hidden Markov models (HMM). We use a novel type of digitizer tablet and pay special attention to the use of pen-tilt. We investigate the verification reliability based on different forgery types. We compare the discriminative value of the different features based on a linear discriminant analysis (LDA) and show that pen-tilt is important. On the basis of home-improved, over-the-shoulder and professional forgeries, we show that the amount of dynamic information available to an imposter is important and that forgeries based on paper copies are easier to detect. The results obtained with a database of almost 5000 signatures of 51 persons with highly skilled forgeries include equal-error rates between 1% and 1.9%.",

author = "J.G.A. Dolfing and E.H.L. Aarts and {Oosterhout, van}, J.J.G.M.",

year = "1998",

doi = "10.1109/ICPR.1998.711942",

language = "English",

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booktitle = "14th International Conference on Pattern Recognition (Brisbane, Queensland, Australia, August 16-20, 1998)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

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On-line signature verification with hidden Markov models. / Dolfing, J.G.A.; Aarts, E.H.L.; Oosterhout, van, J.J.G.M.

14th International Conference on Pattern Recognition (Brisbane, Queensland, Australia, August 16-20, 1998). Piscataway : Institute of Electrical and Electronics Engineers, 1998. blz. 1309-1312.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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N2 - This paper addresses the problem of online signature verification based on hidden Markov models (HMM). We use a novel type of digitizer tablet and pay special attention to the use of pen-tilt. We investigate the verification reliability based on different forgery types. We compare the discriminative value of the different features based on a linear discriminant analysis (LDA) and show that pen-tilt is important. On the basis of home-improved, over-the-shoulder and professional forgeries, we show that the amount of dynamic information available to an imposter is important and that forgeries based on paper copies are easier to detect. The results obtained with a database of almost 5000 signatures of 51 persons with highly skilled forgeries include equal-error rates between 1% and 1.9%.

AB - This paper addresses the problem of online signature verification based on hidden Markov models (HMM). We use a novel type of digitizer tablet and pay special attention to the use of pen-tilt. We investigate the verification reliability based on different forgery types. We compare the discriminative value of the different features based on a linear discriminant analysis (LDA) and show that pen-tilt is important. On the basis of home-improved, over-the-shoulder and professional forgeries, we show that the amount of dynamic information available to an imposter is important and that forgeries based on paper copies are easier to detect. The results obtained with a database of almost 5000 signatures of 51 persons with highly skilled forgeries include equal-error rates between 1% and 1.9%.

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