Off-Line Signature Verification by Local Granulometric Size Distributions
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
Offline Geometric Parameters for Automatic Signature Verification Using Fixed-Point Arithmetic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of Exterior Contours and Shape Features in Off-line Signature Verification
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
A New Off-line Signature Verification Method based on Graph
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Automatic Signature Verification: The State of the Art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Writer-independent off-line signature verification using surroundedness feature
Pattern Recognition Letters
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In this work, we address off-line signature verification as a writer-independent system. We propose a set of morphological features, extracted from off-line signature images. To examine the effectiveness of the features, a publicly available signature database, namely CEDAR signature database is used. A pair of signatures is fed to the system to give an inference for their (dis)similarity. To get a compact set of features, a multilayer perceptron based feature analysis technique is utilized. A 10-fold cross-validation framework based on support vector machine is used for verification. Receiver operator curve (ROC) analysis gives an equal error rate (EER) of 11.59%, which is comparable to the state-of-the-arts reported on this database.