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

Gadaleta, Matteo, Rossi, Michele (2018) IDNet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recognition, 74. 25-37 doi:10.1016/j.patcog.2017.09.005

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleIDNet: Smartphone-based gait recognition with convolutional neural networks
JournalPattern Recognition
AuthorsGadaleta, MatteoAuthor
Rossi, MicheleAuthor
Year2018 (February)Volume74
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2017.09.005Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9367158Long-form Identifiermindat:1:5:9367158:5
GUID0
Full ReferenceGadaleta, Matteo, Rossi, Michele (2018) IDNet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recognition, 74. 25-37 doi:10.1016/j.patcog.2017.09.005
Plain TextGadaleta, Matteo, Rossi, Michele (2018) IDNet: Smartphone-based gait recognition with convolutional neural networks. Pattern Recognition, 74. 25-37 doi:10.1016/j.patcog.2017.09.005
In(2018) Pattern Recognition Vol. 74. Elsevier BV

See Also

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

Yousef, Mohamed, Hussain, Khaled F., Mohammed, Usama S. (2020) Accurate, data-efficient, unconstrained text recognition with convolutional neural networks. Pattern Recognition, 108. 107482pp. doi:10.1016/j.patcog.2020.107482
Shi, Cheng, Pun, Chi-Man (2018) Superpixel-based 3D deep neural networks for hyperspectral image classification. Pattern Recognition, 74. 600-616 doi:10.1016/j.patcog.2017.09.007
Sezavar, Ahmadreza, Atta, Randa, Ghanbari, Mohammed (2024) DCapsNet: Deep capsule network for human activity and gait recognition with smartphone sensors. Pattern Recognition, 147. 110054 doi:10.1016/j.patcog.2023.110054
Sheng, Weijie, Li, Xinde (2021) Multi-task learning for gait-based identity recognition and emotion recognition using attention enhanced temporal graph convolutional network. Pattern Recognition, 114. 107868pp. doi:10.1016/j.patcog.2021.107868
Zhang, Yifan, Shi, Lei, Wu, Yi, Cheng, Ke, Cheng, Jian, Lu, Hanqing (2020) Gesture recognition based on deep deformable 3D convolutional neural networks. Pattern Recognition, 107. 107416pp. doi:10.1016/j.patcog.2020.107416
Ijjina, Earnest Paul, Chalavadi, Krishna Mohan (2016) Human action recognition using genetic algorithms and convolutional neural networks. Pattern Recognition, 59. 199-212 doi:10.1016/j.patcog.2016.01.012
Chen, Changhong, Liang, Jimin, Zhu, Xiuchang (2011) Gait recognition based on improved dynamic Bayesian networks. Pattern Recognition, 44 (4). 988-995 doi:10.1016/j.patcog.2010.10.021
Li, Xin, Jie, Zequn, Feng, Jiashi, Liu, Changsong, Yan, Shuicheng (2018) Learning with rethinking: Recurrently improving convolutional neural networks through feedback. Pattern Recognition, 79. 183-194 doi:10.1016/j.patcog.2018.01.015
Jiao, Zhicheng, Gao, Xinbo, Wang, Ying, Li, Jie, Xu, Haojun (2018) Deep Convolutional Neural Networks for mental load classification based on EEG data. Pattern Recognition, 76. 582-595 doi:10.1016/j.patcog.2017.12.002
Zhang, Zhihong, Chen, Dongdong, Wang, Jianjia, Bai, Lu, Hancock, Edwin R. (2019) Quantum-based subgraph convolutional neural networks. Pattern Recognition, 88. 38-49 doi:10.1016/j.patcog.2018.11.002
Fanfani, Marco, Piva, Alessandro, Colombo, Carlo (2022) PRNU registration under scale and rotation transform based on convolutional neural networks. Pattern Recognition, 124. 108413pp. doi:10.1016/j.patcog.2021.108413
 
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 Β© 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 27, 2025 12:17:02
Go to top of page