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Impact of signature...
Impact of signature legibility and signature type in off-line signature verification
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- Alonso-Fernandez, Fernando (author)
- Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain,ATVS/Biometric Recognition Group
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- Fairhurst, M. (author)
- University of Kent, UK
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- Fierrez, J. (author)
- Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain,ATVS/Biometric Recognition Group
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- Ortega-Garcia, J. (author)
- Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain,ATVS/Biometric Recognition Group
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Escuela Politecnica Superior, Univ Autonoma de Madrid, Spain ATVS/Biometric Recognition Group (creator_code:org_t)
- Piscataway, N.J. IEEE Press, 2007
- 2007
- English.
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In: Biometrics Symposium, 2007. - Piscataway, N.J. : IEEE Press. - 9781424415496 ; , s. 1-6
- Related links:
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https://hh.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- The performance of two popular approaches for off-line signature nature verification in terms of signature legibility and signature type is studied. We investigate experimentally if the knowledge of letters, syllables or name instances can help in the process of imitating a signature. Experimental results are given on a sub-corpus of the MCYT signature database for random and skilled forgeries. We use for our experiments two machine experts, one based on global image analysis and statistical distance measures, and the second based on local image analysis and Hidden Markov Models. Verification results are reported in terms of Equal Error Rate (EER), False Acceptance Rate (FAR) and False Rejection Rate (FRR). ©2007 IEEE.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Keyword
- Biometrics
- Error analysis
- Image analysis
- Imaging techniques
- Learning systems
- Markov processes
- Speech recognition
Publication and Content Type
- ref (subject category)
- kon (subject category)
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