Principles of interactive computer graphics (2nd ed.)
Principles of interactive computer graphics (2nd ed.)
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Learning Strategies and Classification Methods for Off-Line Signature Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
A comparison of SVM and HMM classifiers in the off-line signature verification
Pattern Recognition Letters
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Classifier combination based on confidence transformation
Pattern Recognition
Off-line signature verification and forgery detection using fuzzy modeling
Pattern Recognition
Improved class statistics estimation for sparse data problems in offline signature verification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatic Signature Verification: The State of the Art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A multiresolution approach to computer verification of handwritten signatures
IEEE Transactions on Image Processing
A writer-independent off-line signature verification system based on signature morphology
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Dynamic ensemble selection for off-line signature verification
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Writer-independent off-line signature verification using surroundedness feature
Pattern Recognition Letters
A multi-classifier system for off-line signature verification based on dissimilarity representation
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
A study on the consistency and significance of local features in off-line signature verification
Pattern Recognition Letters
Texture-based descriptors for writer identification and verification
Expert Systems with Applications: An International Journal
Off-line hand written input based identity determination using multi kernel feature combination
Pattern Recognition Letters
An automatic method for construction of ensembles to time series prediction
International Journal of Hybrid Intelligent Systems
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In this work we address two important issues of off-line signature verification. The first one regards feature extraction. We introduce a new graphometric feature set that considers the curvature of the most important segments, perceptually speaking, of the signature. The idea is to simulate the shape of the signature by using Bezier curves and then extract features from these curves. The second important aspect is the use of an ensemble of classifiers based on graphometric features to improve the reliability of the classification, hence reducing the false acceptance. The ensemble was built using a standard genetic algorithm and different fitness functions were assessed to drive the search. Two different scenarios were considered in our experiments. In the former, we assume that only genuine signatures and random forgeries are available to guide the search. In the latter, on the other hand, we assume that simple and simulated forgeries also are available during the optimization of the ensemble. The pool of base classifiers is trained using only genuine signatures and random forgeries. Thorough experiments were conduct on a database composed of 100 writers and the results compare favorably.