Title | ||
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A Linear-complexity Multi-biometric Forensic Document Analysis System, by Fusing the Stylome and Signature Modalities. |
Abstract | ||
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Forensic Document Analysis (FDA) addresses the problem of finding the authorship of a given document. Identification of the document writer via a number of its modalities (e.g. handwriting, signature, linguistic writing style (i.e. stylome), etc.) has been studied in the FDA state-of-the-art. But, no research is conducted on the fusion of stylome and signature modalities. In this paper, we propose such a bimodal FDA system (which has vast applications in judicial, police-related, and historical documents analysis) with a focus on time-complexity. The proposed bimodal system can be trained and tested with linear time complexity. For this purpose, we first revisit Multinomial Na\"ive Bayes (MNB), as the best state-of-the-art linear-complexity authorship attribution system and, then, prove its superior accuracy to the well-known linear-complexity classifiers in the state-of-the-art. Then, we propose a fuzzy version of MNB for being fused with a state-of-the-art well-known linear-complexity fuzzy signature recognition system. For the evaluation purposes, we construct a chimeric dataset, composed of signatures and textual contents of different letters. Despite its linear-complexity, the proposed multi-biometric system is proven to meaningfully improve its state-of-the-art unimodal counterparts, regarding the accuracy, F-Score, Detection Error Trade-off (DET), Cumulative Match Characteristics (CMC), and Match Score Histograms (MSH) evaluation metrics. |
Year | Venue | DocType |
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2019 | arXiv: Computation and Language | Journal |
Volume | Citations | PageRank |
abs/1902.02176 | 0 | 0.34 |
References | Authors | |
30 | 10 |
Name | Order | Citations | PageRank |
---|---|---|---|
Sayyed-Ali Hossayni | 1 | 0 | 0.68 |
Yousef Alizadeh-Q | 2 | 0 | 0.34 |
Vahid Tavana | 3 | 0 | 0.34 |
Seyed M. Hosseini Nejad | 4 | 0 | 0.34 |
Mohammad-R. Akbarzadeh-T | 5 | 85 | 12.94 |
Esteve del Acebo | 6 | 76 | 11.10 |
Josep Lluís de la Rosa i Esteva | 7 | 0 | 0.34 |
Enrico Grosso | 8 | 396 | 43.12 |
Massimo Tistarelli | 9 | 939 | 81.95 |
Przemyslaw Kudlacik | 10 | 0 | 0.68 |