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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]
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).
^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. S2CID17863249.
^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.
^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. ISBN978-3-540-22146-3.
^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 (inactive 12 July 2025).{{cite journal}}: CS1 maint: DOI inactive as of July 2025 (link)
