Name
Abstract | ||
|---|---|---|
Nowadays, with the increase of biometric studies, the diversity of biometric data increases and new methods arc used in evaluation methods. Traditional biometrics, such as face, fingerprints, handpieces, now leave their place to a variety of biometrics, which contain characteristic information about more people and include movement information. In this study, the performance of the deep learning method based on convolutional neural network (CNN) is demonstrated on a nonlinear signature recognition problem. In this non-real-time signature recognition application, it has been tried to reduce the process load and memory requirement by using deep learning method. Two data sets with different participant numbers were created in the study. The performance and reliability of the system are examined by various ratios of training and testing data on these data sets. |
Year | Venue | Keywords |
|---|---|---|
2017 | Signal Processing and Communications Applications Conference | biometric,deep learning,convolutional artificial neural network,signature recognition |
Field | DocType | ISSN |
Neocognitron,Signature recognition,Pattern recognition,Intelligent character recognition,Convolutional neural network,Computer science,Recurrent neural network,Artificial intelligence,Deep learning,Biometrics,Artificial neural network,Machine learning | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Calik, Nurullah | 1 | 2 | 2.42 |
| Onur Can Kurban | 2 | 1 | 1.72 |
| Ali Riza Yilmaz | 3 | 1 | 1.71 |
| Lutfiye Durak-Ata | 4 | 46 | 13.89 |
| Tulay Yildirim | 5 | 49 | 14.25 |