Signature recognition explained

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:

• 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]

Notes and References

1. Ismail. M.A.. Gad. Samia. Oct 2000. Off-line arabic signature recognition and verification. Pattern Recognition. 33. 10. 1727–1740. 10.1016/s0031-3203(99)00047-3. 2000PatRe..33.1727I . 0031-3203.
2. Web site: 2016-01-11. Explainer: Signature Recognition Biometric Update. 2021-04-03. www.biometricupdate.com. en-US.
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 . BioSecure signature evaluation campaign (BSEC'2009): Evaluating online signature algorithms depending on the quality of signatures. Pattern Recognition. March 2012. 45. 3. 993–1003. 10.1016/j.patcog.2011.08.008. 2012PatRe..45..993H . 17863249 .
4. Faundez-Zanuy. Marcos. On-line signature recognition based on VQ-DTW. Pattern Recognition. 2007. 40. 3. 981–992. 10.1016/j.patcog.2006.06.007. 2007PatRe..40..981F .
5. Chapran. J.. Biometric Writer Identification: Feature Analysis and Classification. International Journal of Pattern Recognition & Artificial Intelligence. 2006. 20. 4. 483–503. 10.1142/s0218001406004831.
6. Book: Yeung, D. H. . Xiong, Y. . George, S. . Kashi, R. . Matsumoto, T. . Rigoll, G. . Biometric Authentication . SVC2004: First International Signature Verification Competition . Lecture Notes in Computer Science . 3072 . 2004 . 16–22. 10.1007/978-3-540-25948-0_3 . 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 . MCYT baseline corpus: A bimodal biometric database . IEE Proceedings - Vision, Image, and Signal Processing . 150. 6 . 395–401. 10.1049/ip-vis:20031078. 2003 .