Twin Convolutional Neural Networks to Classify Writers Using Handwriting Data
| dc.contributor.author | Lim, Andrew | |
| dc.contributor.author | Ommen, Danica | |
| dc.contributor.department | Center for Statistics and Applications in Forensic Evidence | |
| dc.contributor.department | Statistics (LAS) | |
| dc.date.accessioned | 2022-11-10T13:58:04Z | |
| dc.date.available | 2022-11-10T13:58:04Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Primary goals are to examine: 1. Write diversification versus representation. 2. Preservation of handwriting structure versus image density. 3. Input size versus training size. 4. Writer identification complexity assessment using various test sites. | |
| dc.description.comments | The following poster was presented at the 106th International Association for Identification (IAI) Annual Educational Conference, Omaha, Nebraska, July 31-Aug 6, 2022. Posted with permission of CSAFE. | |
| dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/erLKZYnv | |
| dc.language.iso | en | |
| dc.publisher | Copyright 2022, The Authors | |
| dc.subject.disciplines | DegreeDisciplines::Social and Behavioral Sciences::Legal Studies::Forensic Science and Technology | |
| dc.title | Twin Convolutional Neural Networks to Classify Writers Using Handwriting Data | |
| dc.type | Presentation | |
| dspace.entity.type | Publication | |
| relation.isOrgUnitOfPublication | d8a3c72b-850f-40f6-87c4-8812547080c7 | |
| relation.isOrgUnitOfPublication | 264904d9-9e66-4169-8e11-034e537ddbca |
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