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 an example of behavioral biometrics that identifies a person based on their handwriting. It can be operated in two different ways:

Static: In this mode, users write their signature on paper, and after the writing is complete, it is digitized through an optical scanner or a camera to turn the signature image into bits.[1] The biometric system then recognizes the signature analyzing its shape. This group is also known as "off-line".[2]

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:[2]

  • 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.[3]

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.[4]

Recently, a handwritten biometric approach has also been proposed.[5] 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,[6] and MCYT.[7]

References

  1. ↑ Ismail, M.A.; Gad, Samia (Oct 2000). "Off-line arabic signature recognition and verification". Pattern Recognition. 33 (10): 1727–1740. Bibcode:2000PatRe..33.1727I. doi:10.1016/s0031-3203(99)00047-3. ISSN 0031-3203.
  2. 1 2 "Explainer: Signature Recognition | Biometric Update". www.biometricupdate.com. 2016-01-11. Retrieved 2021-04-03.
  3. ↑ 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. Bibcode:2012PatRe..45..993H. doi:10.1016/j.patcog.2011.08.008. S2CID 17863249.
  4. ↑ Faundez-Zanuy, Marcos (2007). "On-line signature recognition based on VQ-DTW". Pattern Recognition. 40 (3): 981–992. Bibcode:2007PatRe..40..981F. doi:10.1016/j.patcog.2006.06.007.
  5. ↑ 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.
  6. ↑ Yeung, D. H.; Xiong, Y.; George, S.; Kashi, R.; Matsumoto, T.; Rigoll, G. (2004). "SVC2004: First International Signature Verification Competition". Biometric Authentication. Lecture Notes in Computer Science. Vol. 3072. pp. 16–22. doi:10.1007/978-3-540-25948-0_3. ISBN 978-3-540-22146-3.
  7. ↑ 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 bimodal biometric database". IEE Proceedings - Vision, Image, and Signal Processing. 150 (6): 395–401. doi:10.1049/ip-vis:20031078.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.