Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10361550 | Pattern Recognition Letters | 2005 | 9 Pages |
Abstract
The SVM is a new classification technique in the field of statistical learning theory which has been applied with success in pattern recognition applications like face and speaker recognition, while the HMM has been found to be a powerful statistical technique which is applied to handwriting recognition and signature verification. This paper reports on a comparison of the two classifiers in off-line signature verification. For this purpose, an appropriate learning and testing protocol was created to observe the capability of the classifiers to absorb intrapersonal variability and highlight interpersonal similarity using random, simple and simulated forgeries.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Edson J.R. Justino, Flávio Bortolozzi, Robert Sabourin,