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
On-line Handwritten Signature Verification using Hidden Markov Model Features
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
On-Line Signature Verification with Hidden Markov Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Gaussian Mixture Models for on-line signature verification
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
HMM-based on-line signature verification: Feature extraction and signature modeling
Pattern Recognition Letters
A writer identification system for on-line whiteboard data
Pattern Recognition
Expert Systems with Applications: An International Journal
A comparative evaluation of fusion strategies for multimodal biometric verification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
An on-line signature verification system based on fusion of local and global information
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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
Effectiveness of pen pressure, azimuth, and altitude features for online signature verification
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Automatic online signature verification using HMMs with user-dependent structure
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In this contribution a function-based approach to on-line signature verification is presented. An initial set of 8 time sequences is used; then first and second time derivates of each function are computed over these, so 24 time sequences are simultaneously considered. A valuable function normalization is applied as a previous stage to a continuous-density HMM-based complete signal modeling scheme of these 24 functions, so no derived statistical features are employed, fully exploiting in this manner the HMM modeling capabilities of the inherent time structure of the dynamic process. In the verification stage, scores are considered not as absolute but rather as relative values with respect to a reference population, permitting the use of a best-reference score-normalization technique. Results using MCYT_Signature sub-corpus on 50 clients are presented, attaining an outstanding best figure of 0.35% EER for skilled forgeries, when signer-dependent thresholds are considered.