Fundamentals of speech recognition
Fundamentals of speech recognition
Reliable On-Line Human Signature Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting Handwritten Signatures at Their Perceptually Important Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line Handwritten Signature Verification using Hidden Markov Model Features
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
On-Line Signature Verification With Two-Stage Statistical Models
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Offline signature authentication using cross-validated graph matching
Proceedings of the 2nd Bangalore Annual Compute Conference
Online handwriting Mongolia words recognition based on HMM classifier
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Rough set approach to online signature identification
Digital Signal Processing
An efficient online signature verification scheme using dynamic programming of string matching
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
On-Line signature verification based on dynamic bayesian network
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
On-line writer verification using force features of basic strokes
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
A study on enhanced dynamic signature verification for the embedded system
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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In this paper, a new on-line handwritten signatureverification system using Hidden Markov Model (HMM)is presented. The proposed system segments eachsignature based on its perceptually important points andthen computes for each segment a number of features thatare scale and displacement invariant. The resultedsequence is then used for training an HMM to achievesignature verification. Our database includes 622genuine signatures and 1010 forgery signatures that werecollected from a population of 69 human subjects. Ourverification system has achieved a false acceptance rate(FAR) of 4% and a false rejection rate (FRR) of 12%.