A writer-independent approach for offline signature verification using deep convolutional neural networks features
This work addresses the problem of verifying handwritten signatures for security applications, offering a more scalable solution by reducing the need for per-writer training, though it is incremental as it builds on prior CNN-based methods.
The paper tackled offline signature verification by proposing a writer-independent approach using deep CNN features with an SVM classifier, achieving performance improvements over existing methods and matching writer-dependent results in some scenarios.
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. In this work it is investigated whether the use of these CNN features provide good results in a writer-independent (WI) HSV context, based on the dichotomy transformation combined with the use of an SVM writer-independent classifier. The experiments performed in the Brazilian and GPDS datasets show that (i) the proposed approach outperformed other WI-HSV methods from the literature, (ii) in the global threshold scenario, the proposed approach was able to outperform the writer-dependent method with CNN features in the Brazilian dataset, (iii) in an user threshold scenario, the results are similar to those obtained by the writer-dependent method with CNN features.