Signature recognition

From Wikipedia, the free encyclopedia
Jump to: navigation, search
Example of dynamic signature. Its dynamic information is shown on the right.
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. 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]

Related techniques[edit]

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

Databases[edit]

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

References[edit]

  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: 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: 16–22. 
  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. "MCYT Baseline Corpus: A Multimodal Biometric Database". IEE Proceedings - Vision, Image and Signal Processing 150: 395–401. doi:10.1049/ip-vis:20031078.