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 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'.
In the dynamic footprint approach, the footprint of a subject on the move is used to identify an individual. When the recognition technology comes close to its maturity, it is expected to use features like foot shape, texture, friction ridge, etc. Dynamic Signature Recognition and Verification Using Pixel Based Approach. Thinning languages, such as C/C or Java®. MATLAB can be use for a range of applications, including signal processing and I. INTRODUCTION communications, image and video processing, control Authentication is the primitive thing when it comes to any systems, test.
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.[1]
![Signature Signature](/uploads/1/2/5/6/125620455/620138318.jpg)
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 handwritten 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]
- ^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.
- ^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.
- ^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'. Lecture Notes in Computer Science. LNCS-3072. 3072: 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.
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