What Size Test Set Gives Good Error Rate Estimates?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Spectrum Analysis Based onWindows with Variable Widths for Online Signature Verification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
On-line signature recognition based on VQ-DTW
Pattern Recognition
Signature verification (SV) toolbox: Application of PSO-NN
Engineering Applications of Artificial Intelligence
Practical On-Line Signature Verification
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Online signature verification with support vector machines based on LCSS kernel functions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
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
On Using the Viterbi Path Along With HMM Likelihood Information for Online Signature Verification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic signature recognition based on fisher discriminant
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used the MCYT database, which consists of 330 users and 25 skilled forgeries per person performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system outperforms the winner of SVC with a reduced computational requirement, which is around 47 times lower than DTW. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly as it is not possible to recover the original signature using the codebooks. Experimental results with MCYT provide a 99.76% identification rate and 2.46% EER (skilled forgeries and individual threshold). Experimental results with SVC are 100% of identification rate and 0% (individual threshold) and 0.31% (general threshold) when using a two-section VQ approach.