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Found 4 papers, 0 papers with code
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification
We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation.
Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control
This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV).
A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification
Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context.
A writer-independent approach for offline signature verification using deep convolutional neural networks features
The use of features extracted using a deep convolutional neural network (CNN) combined with a writer-dependent (WD) SVM classifier resulted in significant improvement in performance of handwritten signature verification (HSV) when compared to the previous state-of-the-art methods.
