Watch the Dallas Symposium LIVE, and fundraiser auction
Ticket proceeds support mindat.org! - click here...
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Hafemann, Luiz G., Sabourin, Robert, Oliveira, Luiz S. (2017) Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recognition, 70. 163-176 doi:10.1016/j.patcog.2017.05.012

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleLearning features for offline handwritten signature verification using deep convolutional neural networks
JournalPattern Recognition
AuthorsHafemann, Luiz G.Author
Sabourin, RobertAuthor
Oliveira, Luiz S.Author
Year2017 (October)Volume70
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2017.05.012Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9367038Long-form Identifiermindat:1:5:9367038:2
GUID0
Full ReferenceHafemann, Luiz G., Sabourin, Robert, Oliveira, Luiz S. (2017) Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recognition, 70. 163-176 doi:10.1016/j.patcog.2017.05.012
Plain TextHafemann, Luiz G., Sabourin, Robert, Oliveira, Luiz S. (2017) Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recognition, 70. 163-176 doi:10.1016/j.patcog.2017.05.012
In(2017) Pattern Recognition Vol. 70. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Xiao, Xuefeng, Jin, Lianwen, Yang, Yafeng, Yang, Weixin, Sun, Jun, Chang, Tianhai (2017) Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition. Pattern Recognition, 72. 72-81 doi:10.1016/j.patcog.2017.06.032
Britto, Alceu S., Sabourin, Robert, Oliveira, Luiz E.S. (2014) Dynamic selection of classifiers—A comprehensive review. Pattern Recognition, 47 (11). 3665-3680 doi:10.1016/j.patcog.2014.05.003
Ko, Albert H.R., Sabourin, Robert, Britto, Alceu de Souza, Oliveira, Luiz (2007) Pairwise fusion matrix for combining classifiers. Pattern Recognition, 40 (8). 2198-2210 doi:10.1016/j.patcog.2007.01.031
Zhao, Xingbiao; Zheng, Lidong; Yuan, Panli; Zheng, Yuchen (2025) Fusing deep and hand-crafted features by deep canonically correlated contractive autoencoder for offline signature verification. Pattern Recognition, 168. doi:10.1016/j.patcog.2025.111834
Brun, André L., Britto, Alceu S., Oliveira, Luiz S., Enembreck, Fabricio, Sabourin, Robert (2018) A framework for dynamic classifier selection oriented by the classification problem difficulty. Pattern Recognition, 76. 175-190 doi:10.1016/j.patcog.2017.10.038
Okawa, Manabu (2018) Synergy of foreground–background images for feature extraction: Offline signature verification using Fisher vector with fused KAZE features. Pattern Recognition, 79. 480-489 doi:10.1016/j.patcog.2018.02.027
Zois, Elias N., Alewijnse, Linda, Economou, George (2016) Offline signature verification and quality characterization using poset-oriented grid features. Pattern Recognition, 54. 162-177 doi:10.1016/j.patcog.2016.01.009
Batista, Luana, Granger, Eric, Sabourin, Robert (2012) Dynamic selection of generative–discriminative ensembles for off-line signature verification. Pattern Recognition, 45 (4). 1326-1340 doi:10.1016/j.patcog.2011.10.011
Yang, Weixin, Jin, Lianwen, Tao, Dacheng, Xie, Zecheng, Feng, Ziyong (2016) DropSample : A new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition. Pattern Recognition, 58. 190-203 doi:10.1016/j.patcog.2016.04.007
Wu, Yi-Chao, Yin, Fei, Liu, Cheng-Lin (2017) Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models. Pattern Recognition, 65. 251-264 doi:10.1016/j.patcog.2016.12.026
Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R. (2010) Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers. Pattern Recognition, 43 (1). 387-396 doi:10.1016/j.patcog.2009.05.009
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: August 17, 2025 08:11:31
Go to top of page