Multi-representational learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs
作者：
Highlights：
• Multi-Loss Snapshot Ensemble (MLSE) of CNNs offers feature ensemble learning for OSV.
• MLSE proposes simultaneous use of multi-loss function within a sequential training.
• MLSE combines advantage of diversity and regularization to tackle challenges of OSV.
• USMG-SVM combines decisions by selecting most generalizable SVM for each user.
• MLSE + USMG-SVM achieved significant improvements over state-of-the-arts in OSV.
摘要
•Multi-Loss Snapshot Ensemble (MLSE) of CNNs offers feature ensemble learning for OSV.•MLSE proposes simultaneous use of multi-loss function within a sequential training.•MLSE combines advantage of diversity and regularization to tackle challenges of OSV.•USMG-SVM combines decisions by selecting most generalizable SVM for each user.•MLSE + USMG-SVM achieved significant improvements over state-of-the-arts in OSV.
论文关键词：Offline Signature Verification,Convolutional Neural Network,Multi-loss function,Snapshot Ensemble
论文评审过程：Received 8 October 2018, Revised 6 March 2019, Accepted 6 March 2019, Available online 23 March 2019, Version of Record 24 May 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.03.040
