On-line Handwritten Signature Verification using Hidden Markov Model Features
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
An On-Line Signature Verification System Using Hidden Markov Model in Polar Space
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ER2: An Intuitive Similarity Measure for On-Line Signature Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
An HMM on-line signature verification algorithm
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
On-line signature verification using LPC cepstrum and neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The necessity to authenticate individuals is rapidly increasing day by day with the explosive growth of E-commerce, E-finance, PDA, etc. Handwritten signature is the most widely used and easiest way to verify a person. Online signature verification is a very active and hot topic in the field of biometric research. It is a potential candidate to replace traditional password-based security system as the password can be forgotten, stolen or guessed. Online signature verification deals with both spatial and temporal features of signature. Therefore, it is difficult to forge. This paper proposes a novel online signature verification technique using dynamic programming of string matching. The performance of the proposed approach is evaluated for both genuine signatures and skilled forgeries on SVC2004 database. The proposed approach produces a False Acceptance Rate (FAR) of 4.13% and False Rejection Rate (FRR) of 5.5% with an Equal Error Rate (ERR) of 5%.