hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Forensic Writer Identification Using Allographic Features
Universidad Autonoma de Madrid, Spain.
Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain. (ATVS/Biometric Recognition Group)ORCID iD: 0000-0002-1400-346X
Universidad Autonoma de Madrid, Spain.
Universidad Autonoma de Madrid, Spain.
2010 (English)In: Proceedings: 12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010, Los Alamitos, Calif.: IEEE Computer Society, 2010, 308-313 p.Conference paper, (Refereed)
Abstract [en]

Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented and labeled by an expert as pertaining to one of 62 alphanumeric classes (10 numbers and 52 letters, including lowercase and uppercase letters), being the particular setup used by the forensic laboratory participating in this work. A codebook of shapes is then generated by clustering and the probability distribution function of allograph usage is the discriminative feature used for recognition. Results obtained on a database of 30 writers from real forensic documents show that the character class information given by the manual analysis provides a valuable source of improvement, justifying the proposed approach. We also evaluate the selection of different alphanumeric channels, showing a dependence between the size of the hit list and the number of channels needed for optimal performance. © 2010 IEEE.

Place, publisher, year, edition, pages
Los Alamitos, Calif.: IEEE Computer Society, 2010. 308-313 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-21235DOI: 10.1109/ICFHR.2010.54Scopus ID: 2-s2.0-79951683972ISBN: 978-076954221-8 OAI: oai:DiVA.org:hh-21235DiVA: diva2:589361
Conference
12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010, Kolkata, India, 16-18 November, 2010
Available from: 2013-01-17 Created: 2013-01-17 Last updated: 2015-09-29Bibliographically approved

Open Access in DiVA

fulltext(965 kB)82 downloads
File information
File name FULLTEXT02.pdfFile size 965 kBChecksum SHA-512
b658f8ca11eedec92afc98cf70a401825d6af0a6d4c9a0209efd2c159ccb1d630d54da2f9423e7df141a10a82cb8e19bb764b5ce1b4fe6621efc03efa9478797
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Alonso-Fernandez, Fernando
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 82 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 85 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf