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Verification of dynamic signature using machine learning approach.
- Source :
-
Neural Computing & Applications . Aug2020, Vol. 32 Issue 15, p11875-11895. 21p. - Publication Year :
- 2020
-
Abstract
- This paper presents a novel approach for dynamic signature authentication based on the machine learning approach. In the proposed method, average values of features are taken into consideration for the verification. Here, seven different types (x and y coordinates, time stamp, pen ups and downs, azimuth, altitude and pressure) of features are used. The obtained extracted feature is learned into different classifiers. Different classifiers have been taken into consideration like random tree, Naive Bayes, random forest, J48, etc. These features are extracted from well-known SVC2004 dataset. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*TIMESTAMPS
*AZIMUTH
*BIOMETRIC identification
*ALTITUDES
Subjects
Details
- Language :
- English
- ISSN :
- 09410643
- Volume :
- 32
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- Neural Computing & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 144642772
- Full Text :
- https://doi.org/10.1007/s00521-019-04669-w