Journal of Computer Science

Feature Discretization for Individuality Representation in Twins Handwritten Identification

Bayan Omar Mohammed and Siti Mariyam Shamsuddin

DOI : 10.3844/jcssp.2011.1080.1087

Journal of Computer Science

Volume 7, Issue 7

Pages 1080-1087


Problem statement: The study on twins is an important form of study in the forensic and biometrics field as twins share similar genetic traits. Handwriting is one of the common types of forensic evidence. Differentiating the similarities of writing of a pair of twins is critical in establishing the reliability of handwriting identification. Writing style can be used as biometric features in authenticating individual uniqueness where these unique features can be used to identify the writer, including between a pair of twins. Existing works in Writer Identification concentrate on feature extraction and the classification task in order to identify authorship. The high similarity in a pair of twins’ handwriting may degrade classification performance. There should be some standards to represent these unique features before entering into the classification task which is with the use of discretization technique. Approach: We proposed a new framework for writer identification in terms of identifying twins’ handwriting and showed the effect of discretization process on handwriting samples of a pair of twins in order to obtain individual identification. Results: An experiment has been done at the Sulaimania University in Iraq with fourteen pairs of identical twins where each twin provides 4 samples of handwriting for the purpose of data collecting. These samples were implemented in this research making a comparison between the new proposed framework and classic framework. Conclusion: Our experimental results showed that with new framework identification of handwriting of a pair of twins can be improved through the discretization process.


© 2011 Bayan Omar Mohammed and Siti Mariyam Shamsuddin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.