Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification using SIFT and RootSIFT Descriptors
- Submitting institution
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University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22063009
- Type
- D - Journal article
- DOI
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10.1109/TIFS.2018.2850011
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 289
- Volume
- 14
- Issue
- 2
- ISSN
- 1556-6013
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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E - Intelligent Systems Research Group (iSRG)
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Poor quality or illegible handwriting remains one the most challenging problems in handwriting forensics. This paper proposes a novel weighted dissimilarity mixture model to efficiently extract key discriminative handwriting descriptors. It received best paper award at the 2016 IEEE Workshop on Visual Information Processing (EUVIP)- European Association for Signal Processing (EURASIP) (Marseille, France). The system is applicable to Latin, Arabic and Hybrid languages and the algorithm has been integrated as a module in the hybrid forensic system by the Qatar Security Office (contact: kalsada@moi.gov.qa). Research supported by Qatar Science Foundation (NPRP7-442-1-082) with Qatar University and Ecole Superieure de Technologie (Canada).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -