Please use this identifier to cite or link to this item: https://accedacris.ulpgc.es/handle/10553/139859
Title: Exploring textural features for handwriting-based personality assessment: an experimental study
Authors: Gahmousse, Abdellatif
Djeddi, Chawki
Gattal, Abdeljalil
Cheddad, Abbas
Diaz, Moises 
UNESCO Clasification: 33 Ciencias tecnológicas
120601 Construcción de algoritmos
Keywords: Combined classification
Five factor model
Personality traits
Textural feature
Issue Date: 2025
Journal: Signal, Image and Video Processing 
Abstract: Personality trait identification through handwriting analysis presents a challenging area within automated document recognition based on Artificial Intelligence solutions. Recent studies relied on solutions automating graphonomic processes, while others address only a few local features, conversely few studies offer solutions based on textural features. In this work, we propose an automated approach for personality trait identification that treats a scripter’s handwriting as a texture by leveraging a diverse set of textural features, including LCP, oBIFCs, LPQ, LBP, among others. The approach is validated on FFM-annotated datasets using cost-effective classifiers such as XGBoost, Random Forest, Gradient Boost, SVM, and Naive Bayes. Our empirical study enabled the judicious selection of the most suitable textural features for each personality trait. Subsequently, we constructed a comprehensive personality trait identification solution by combining multiple textural features and integrating top-performing classifiers. The experimental results demonstrated the validity of our hypothesis, achieving performance improvements of more than 10% on both datasets.
URI: https://accedacris.ulpgc.es/handle/10553/139859
ISSN: 1863-1703
DOI: 10.1007/s11760-025-04061-3
Source: Signal, Image and Video Processing [ISSN 1863-1703],v. 19 (6), (Junio 2025)
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