The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON MATHEMATICS
Transactions ID Number: 29-576
Full Name: Bence Kovari
Position: Ph.D. Candidate
Age: ON
Sex: Male
Address: 1111
Country: HUNGARY
Tel: 0036304465647
Tel prefix:
Fax:
E-mail address: beny@aut.bme.hu
Other E-mails: kovari@gmail.com
Title of the Paper: Classification Approaches in Off-Line Handwritten Signature Verification
Authors as they appear in the Paper: Bence Kovari, Benedek Toth, Hassan Charaf
Email addresses of all the authors: beny@gmail.com
Number of paper pages: 10
Abstract: The aim of off-line signature verification is to decide, whether a signature originates from a given signer based on the scanned image of the signature and a few images of the original signatures of the signer. Although the verification process can be thought to as a monolith component, it is recommended to divide it into loosely coupled phases (like preprocessing, feature extraction, feature matching, feature comparison and classification) allowing us to gain a better control over the precision of different components. This paper focuses on classification, the last phase in the process, covering some of the most important general approaches in the field. Each approach is evaluated for applicability in signature verification, identifying their strength and weaknesses. It is shown, that some of these weak points are common between the different approaches and can partially be eliminated with our proposed solutions. To demonstrate this, several local features are in!
troduced and compared using different classification approaches. Results are evaluated on the database of the Signature Verification Competition 2004.
Keywords: signature verification; off-line; classification, shape descriptor, neural network
EXTENSION of the file: .pdf
Special (Invited) Session: Local Feature Based Off-line Signature Verification using Neural Network Classifiers
Organizer of the Session: 624-313
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