On the Individuality of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval Based Writer Identification
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A writer identification and verification system
Pattern Recognition Letters
Now what was that password again? A more flexible way of identifying and authenticating our seniors
Behaviour & Information Technology - Designing Computer Systems for and with Older Users
Latent Dirichlet allocation based writer identification in offline handwriting
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Analysis of intra-person variability of features for off-line signature verification
WSEAS Transactions on Computers
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
A set of geometrical features for writer identification
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Understanding the consistency of users' pen and finger stroke gesture articulation
Proceedings of Graphics Interface 2013
Off-line hand written input based identity determination using multi kernel feature combination
Pattern Recognition Letters
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Abstract: Motivated by several rulings in United States courts concerning expert testimony in general and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individualistic. Handwriting samples of 1,500 individuals, representative of the US population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by expert document examiners, were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the expert document examiner.