Gaussian Mixture Models for on-line signature verification
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Secure Biometric Authentication with Improved Accuracy
ACISP '08 Proceedings of the 13th Australasian conference on Information Security and Privacy
Development of a Sigma-Lognormal representation for on-line signatures
Pattern Recognition
A global feature for on-line signature verification
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
A survey of on-line signature verification
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Online signature verification with new time series kernels for support vector machines
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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
Off-Line signature verification based on directional gradient spectrum and a fuzzy classifier
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Sensor interoperability and fusion in signature verification: a case study using tablet PC
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
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We propose in this work to perform on-line signature verification by the fusion of two complementary verification modules. The first one considers a signature as a sequence of points and models the genuine signatures of a given signer by a Hidden Markov Model (HMM). Forgeries are used to compute a decision threshold. In the second module, global parameters of a signature are the inputs of a two-classes neural network trained for each signer on both the genuine and "other" signatures (genuine signatures of other signers). Fusion of the scores given by these two experts through a Support Vector Machine (SVM), allows improving the results over those of each module, on Philips' Database.