Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel Convolutional Neural Network (CNN) based method that extracts the location information (displacement features) of the maximums in the max-pooling operation and fuses it with the pooling features to capture the micro deformations between the genuine signatures and skilled forgeries as a feature extraction procedure. After the feature extraction procedure, we apply support vector machines (SVMs) as writer-dependent classifiers for each user to build the signature verification system. The extensive experimental results on GPDS-150, GPDS-300, GPDS-1000, GPDS-2000, and GPDS-5000 datasets demonstrate that the proposed method can discriminate the genuine signatures and their corresponding skilled forgeries well and achieve state-of-the-art results on these datasets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDAR.2019.00180 | ICDAR |
Field | DocType | Citations |
Computer vision,Pattern recognition,Convolutional neural network,Computer science,Pooling,Support vector machine,Feature extraction,Artificial intelligence,Fuse (electrical),Verification system | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuchen Zheng | 1 | 0 | 0.68 |
| Wataru Ohyama | 2 | 0 | 0.68 |
| Brian Kenji Iwana | 3 | 7 | 6.58 |
| Seiichi Uchida | 4 | 790 | 105.59 |