  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in judicial, police-related, and historical documents analysis) with a focus on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.
