Biology:Signature recognition

From HandWiki
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]

Related techniques

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. doi:10.1016/s0031-3203(99)00047-3. ISSN 0031-3203. Bibcode: 2000PatRe..33.1727I. http://dx.doi.org/10.1016/s0031-3203(99)00047-3. 
  2. ↑ 2.0 2.1 "Explainer: Signature Recognition | Biometric Update" (in en-US). 2016-01-11. https://www.biometricupdate.com/201601/explainer-signature-recognition. 
  3. ↑ Houmani, Nesmaa; A. Mayoue; S. Garcia-Salicetti; B. Dorizzi; M.I. Khalil; M. Mostafa; H. Abbas; Z.T. Kardkovàcs et al. (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. Bibcode: 2012PatRe..45..993H. 
  4. ↑ 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. Bibcode: 2007PatRe..40..981F. 
  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. 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 et al. (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. 



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