Learning features for offline handwritten signature verification using deep convolutional neural networks

作者：

Highlights：

• We propose formulations for learning features for Offline Signature Verification.

• A novel method that uses knowledge of forgeries from a subset of users is proposed.

• Learned features are used to train classifiers for other users (without forgeries).

• Experiments on GPDS-960 show a large improvement in state-of-the-art.

• Results in other 3 datasets show that the features generalize without fine-tuning.

摘要

•We propose formulations for learning features for Offline Signature Verification.•A novel method that uses knowledge of forgeries from a subset of users is proposed.•Learned features are used to train classifiers for other users (without forgeries).•Experiments on GPDS-960 show a large improvement in state-of-the-art.•Results in other 3 datasets show that the features generalize without fine-tuning.

论文关键词：Signature verification,Convolutional Neural Networks,Feature learning,Deep learning

论文评审过程：Received 6 December 2016, Revised 6 April 2017, Accepted 13 May 2017, Available online 15 May 2017, Version of Record 22 May 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.05.012