NDLI: Deep learning approach to generate offline handwritten signatures based on online samples

Content Provider	IET Digital Library
Author	Melo, Victor K. S. L. Bezerra, Byron Leite Dantas Impedovo, Donato Pirlo, Giuseppe Lundgren, Antonio
Abstract	Reduced training sets are major problems typically found on the task of offline signature verification. To increase the number of samples, the use of synthetic signatures can be taken into account. In this work, a new method for the generation of synthetic offline signatures by using dynamic and static (real) ones is presented. The synthesis is here faced under the perspective of supervised training: the learning model is trained to perform the task of online-to-offline signature conversion. The approach is based on a deep convolutional neural network. The main goal is to enlarge offline training dataset in order to improve performance of the offline signature verification systems. For this purpose, a machine-oriented evaluation on the BiosecurID signature dataset is carried out. The use of synthetic samples (in the training phase) generated with the proposed method on a state-of-the-art classification system exhibits performance similar to those obtained using real signatures; moreover, the combination of real and synthetic signatures in the training set is also able to show improvements of the equal error rate.
Starting Page	215
Ending Page	220
Page Count	6
ISSN	20474938
Volume Number	8
e-ISSN	20474946
Issue Number	Issue 3, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/8/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2018.5091
Journal	IET Biometrics
Publisher Date	2018-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	BiosecurID Signature Dataset Computer Vision And Image Processing Technique Convolution Deep Convolutional Neural Network Deep Learning Approach Feedforward Neural Network Handwriting Recognition Image Classification Image Recognition Learning in AI Neural Computing Technique Offline Handwritten Signatures Verification System Online-to-offline Signature Conversion Supervised Training Synthetic Offline Signatures
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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