2019/01/26 by Sayyed-Ali Hossayni, Hossayni, Sayyed-Ali, Yousef Alizadeh-Q +21 · 1 citation
Computer Science · Mathematics · #Artificial intelligence #Authorship Attribution and Profiling #Biometrics #Computation and Language (cs.CL) #Computer science #Digital Media Forensic Detection #FOS: Computer and information sciences #Mathematics #Modalities #Pattern recognition (psychology) #Signature (topology) #Sociology #Spam and Phishing Detection #cs.CL
paper · pdf · doi:10.48550/arxiv.1902.02176
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/01/26 · openalex publication_date 2019/01/26 · arxiv updated 2019/02/07 · openalex created_date 2019/02/21 · openalex updated_date 2026/07/28
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 In this paper, we propose such a bimodal FDA system (which has vast applications in judicial, police-related, and historical documents analysis) with a focus on time-complexity.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 Naïve 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.