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* pen up/down
* pen up/down


The state-of-the-art in signature recognition can be found in the last major international competition.<ref>{{cite journal|last=Houmani|first=Nesmaa |author2=A. Mayoue |author3=S. Garcia-Salicetti |author4=B. Dorizzi |author5=M.I. Khalil |author6=M. Mostafa |author7=H. Abbas |author8=Z.T. Kardkovàcs |author9=D. Muramatsu |author10=B. Yanikoglu |author11=A. Kholmatov |author12=M. Martinez-Diaz |author13=J. Fierrez |author14=J. Ortega-Garcia |author15=J. Roure Alcobé |author16=J. Fabregas |author17=M. Faundez-Zanuy |author18=J. M. Pascual-Gaspar |author19=V. Cardeñoso-Payo |author20=C. Vivaracho-Pascual |title=BioSecure signature evaluation campaign (BSEC'2009): Evaluating online signature algorithms depending on the quality of signatures|journal=Pattern Recognition|date=March 2012|volume=45|issue=3|pages=993–1003|url=http://www.sciencedirect.com/science/article/pii/S0031320311003220|doi=10.1016/j.patcog.2011.08.008}}</ref>
The state-of-the-art in signature recognition can be found in the last major international competition.<ref>{{cite journal|last=Houmani|first=Nesmaa |author2=A. Mayoue |author3=S. Garcia-Salicetti |author4=B. Dorizzi |author5=M.I. Khalil |author6=M. Mostafa |author7=H. Abbas |author8=Z.T. Kardkovàcs |author9=D. Muramatsu |author10=B. Yanikoglu |author11=A. Kholmatov |author12=M. Martinez-Diaz |author13=J. Fierrez |author14=J. Ortega-Garcia |author15=J. Roure Alcobé |author16=J. Fabregas |author17=M. Faundez-Zanuy |author18=J. M. Pascual-Gaspar |author19=V. Cardeñoso-Payo |author20=C. Vivaracho-Pascual |title=BioSecure signature evaluation campaign (BSEC'2009): Evaluating online signature algorithms depending on the quality of signatures|journal=Pattern Recognition|date=March 2012|volume=45|issue=3|pages=993–1003|doi=10.1016/j.patcog.2011.08.008}}</ref>


The most popular [[pattern recognition]] techniques applied for signature recognition are [[dynamic time warping]], [[hidden Markov model]]s and [[vector quantization]]. Combinations of different techniques also exist.<ref>{{cite journal|last=Faundez-Zanuy|first=Marcos|title=On-line signature recognition based on VQ-DTW|journal=Pattern Recognition|year=2007|volume=40|issue=3|pages=981–992|url=http://www.sciencedirect.com/science/article/pii/S0031320306002780|doi=10.1016/j.patcog.2006.06.007}}</ref>
The most popular [[pattern recognition]] techniques applied for signature recognition are [[dynamic time warping]], [[hidden Markov model]]s and [[vector quantization]]. Combinations of different techniques also exist.<ref>{{cite journal|last=Faundez-Zanuy|first=Marcos|title=On-line signature recognition based on VQ-DTW|journal=Pattern Recognition|year=2007|volume=40|issue=3|pages=981–992|doi=10.1016/j.patcog.2006.06.007}}</ref>


== Related techniques ==
== Related techniques ==
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== Databases ==
== Databases ==
Several public databases exist, being the most popular ones SVC,<ref>{{cite journal|last=Yeung|first=D. H. |author2=Xiong, Y. |author3=George, S. |author4=Kashi, R. |author5=Matsumoto, T. |author6=Rigoll, G. |title=SVC2004: First international signature verification competition|journal=Lecture Notes in Computer Science|year=2004|series=LNCS-3072|pages=16–22}}</ref> and MCYT.<ref>{{cite journal|last=Ortega-Garcia|first=Javier |author2=J. Fierrez |author3=D. Simon |author4=J. Gonzalez |author5=M. Faúndez-Zanuy |author6=V. Espinosa |author7=A. Satue |author8=I. Hernaez |author9=J.-J. Igarza |author10=C. Vivaracho |author11=D. Escudero |author12=Q.-I. Moro |title=MCYT Baseline Corpus: A Multimodal Biometric Database|journal=IEE Proceedings - Vision, Image and Signal Processing|volume=150|issue=6 |pages=395–401|doi=10.1049/ip-vis:20031078|year=2003 }}</ref>
Several public databases exist, being the most popular ones SVC,<ref>{{cite journal|last=Yeung|first=D. H. |author2=Xiong, Y. |author3=George, S. |author4=Kashi, R. |author5=Matsumoto, T. |author6=Rigoll, G. |title=SVC2004: First international signature verification competition|journal=Lecture Notes in Computer Science|volume=3072 |year=2004|series=LNCS-3072|pages=16–22|doi=10.1007/978-3-540-25948-0_3 |isbn=978-3-540-22146-3 }}</ref> and MCYT.<ref>{{cite journal|last=Ortega-Garcia|first=Javier |author2=J. Fierrez |author3=D. Simon |author4=J. Gonzalez |author5=M. Faúndez-Zanuy |author6=V. Espinosa |author7=A. Satue |author8=I. Hernaez |author9=J.-J. Igarza |author10=C. Vivaracho |author11=D. Escudero |author12=Q.-I. Moro |title=MCYT Baseline Corpus: A Multimodal Biometric Database|journal=IEE Proceedings - Vision, Image and Signal Processing|volume=150|issue=6 |pages=395–401|doi=10.1049/ip-vis:20031078|year=2003 }}</ref>


== References ==
== References ==

Revision as of 10:01, 13 July 2019

Example of signature shape.
Example of dynamic information of a signature. Looking at the pressure information it can be seen that the user has lift the pen 3 times in the middle of the signature (areas with pressure equal to zero).

Signature recognition is a behavioural biometric. It can be operated in two different ways:

Static: In this mode, users write their signature on paper, digitize it through an optical scanner or a camera, and the biometric system recognizes the signature analyzing its shape. This group is also known as "off-line".

Dynamic: In this mode, users write their signature in a digitizing tablet, which acquires the signature in real time. Another possibility is the acquisition by means of stylus-operated PDAs. Some systems also operate on smart-phones or tablets with a capacitive screen, where users can sign using a finger or an appropriate pen. Dynamic recognition is also known as "on-line". Dynamic information usually consists of the following information:

  • spatial coordinate x(t)
  • spatial coordinate y(t)
  • pressure p(t)
  • azimuth az(t)
  • inclination in(t)
  • pen up/down

The state-of-the-art in signature recognition can be found in the last major international competition.[1]

The most popular pattern recognition techniques applied for signature recognition are dynamic time warping, hidden Markov models and vector quantization. Combinations of different techniques also exist.[2]

Recently, a handwritten biometric approach has also been proposed.[3] In this case, the user is recognized analyzing his handwritten text (see also Handwritten biometric recognition).

Databases

Several public databases exist, being the most popular ones SVC,[4] and MCYT.[5]

References

  1. ^ Houmani, Nesmaa; A. Mayoue; S. Garcia-Salicetti; B. Dorizzi; M.I. Khalil; M. Mostafa; H. Abbas; Z.T. Kardkovàcs; D. Muramatsu; B. Yanikoglu; A. Kholmatov; M. Martinez-Diaz; J. Fierrez; J. Ortega-Garcia; J. Roure Alcobé; J. Fabregas; M. Faundez-Zanuy; J. M. Pascual-Gaspar; V. Cardeñoso-Payo; C. Vivaracho-Pascual (March 2012). "BioSecure signature evaluation campaign (BSEC'2009): Evaluating online signature algorithms depending on the quality of signatures". Pattern Recognition. 45 (3): 993–1003. doi:10.1016/j.patcog.2011.08.008.
  2. ^ Faundez-Zanuy, Marcos (2007). "On-line signature recognition based on VQ-DTW". Pattern Recognition. 40 (3): 981–992. doi:10.1016/j.patcog.2006.06.007.
  3. ^ Chapran, J. (2006). "Biometric Writer Identification: Feature Analysis and Classification". International Journal of Pattern Recognition & Artificial Intelligence. 20 (4): 483–503. doi:10.1142/s0218001406004831.
  4. ^ Yeung, D. H.; Xiong, Y.; George, S.; Kashi, R.; Matsumoto, T.; Rigoll, G. (2004). "SVC2004: First international signature verification competition". Lecture Notes in Computer Science. LNCS-3072. 3072: 16–22. doi:10.1007/978-3-540-25948-0_3. ISBN 978-3-540-22146-3.
  5. ^ Ortega-Garcia, Javier; J. Fierrez; D. Simon; J. Gonzalez; M. Faúndez-Zanuy; V. Espinosa; A. Satue; I. Hernaez; J.-J. Igarza; C. Vivaracho; D. Escudero; Q.-I. Moro (2003). "MCYT Baseline Corpus: A Multimodal Biometric Database". IEE Proceedings - Vision, Image and Signal Processing. 150 (6): 395–401. doi:10.1049/ip-vis:20031078.
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