Local Binary Patterns for Gender Classification

Abbas, Faycel; Gattal, Abdeljalil; Laouar, Mohamed Ridda; Saoudi, Kamel; Hadjadj, Ismail

doi:10.1145/3330089.3330123

Cited by 2 publications

(1 citation statement)

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“…This study uses statistical textural metrics to categorize writers' gender from scanned images of their handwriting. The gender can be determined from handwriting using LBPs [32], [33] and its derivatives. These include LTP [34], LCP [35], CLBP [36], RLBP [37], LBPV [38], and MLBP [39].…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

Local binary pattern and its derivatives to handwriting-based gender classification

Abbas,

Gattal,

Menassel

2023

Bulletin EEI

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Several studies by psychologists and computer scientists have verified the link between handwriting and writer gender. The texture of the writing image is a major indicator of whether it is male or female writing. This paper conducts a comparison analysis to examine the effectiveness of various local binary patterns (LBPs) techniques in detecting gender from scanned images of handwriting. We study different LBP variants, including complete local binary pattern (CLBP), local ternary pattern (LTP), local configuration pattern (LCP), rotated local binary pattern (RLBP), local binary pattern variance (LBPV), and multi-scale local binary pattern (MLBP), as features for representing handwriting images. A support vector machine (SVM) is trained using features from male and female writing. The method achieves encouraging classification rates of 76.68 when tested on subsets of the Qatar University writer identification (QUWI) dataset containing English and Arabic writing samples when using the experimental protocols of the International Conference on Document Analysis and Recognition (ICDAR) 2013 gender classification competitions.

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