Recurrent binary patterns and CNNs for offline signature verification
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Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer International Publishing Ag
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Signature representations that are extracted by convolutional neural networks (CNN) can achieve low error rates. However, a trade-off exists between such models' complexities and hand-crafted features' slightly higher error rates. A novel writer-dependent (WD) recurrent binary pattern (RBP) network, and a novel signer identification CNN is proposed. RBP network is a recurrent neural network (RNN) to learn the sequential relation between binary pattern histograms over image windows. A novel histogram selection method is introduced to remove the stop-word codes. Dimensionality is reduced by more than 25% while improving the results. This work is the first to combine binary patterns and RNNs for static signature verification. Several test sets, derived from large-scale and popular databases (GPDS-960 and GPDS-Synthetic-10000) are used. Without training any global classifier, RBP network provides competitive equal error rates (EER). The proposed architectures are compared and integrated with other recent CNN models. Score-level integration of WD classifiers trained with different representations are investigated. Cross-validation tests demonstrate the EERs reduced compared to the best single classifier. A state-of-the-art EER of 1.11% is reported with a global decision threshold (0.57% EER with user-based thresholds) on GPDS-160 database.
Açıklama
Future Technologies Conference (FTC) -- OCT 24-25, 2019 -- San Francisco, CA
Yilmaz, Mustafa Berkay/0000-0003-0320-3957
Yilmaz, Mustafa Berkay/0000-0003-0320-3957
Anahtar Kelimeler
Offline signature verification, Recurrent neural network, Convolutional neural network, Score-level integration
Kaynak
Proceedings of the Future Technologies Conference (Ftc) 2019, Vol 2
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
1070
