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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
Review of age and gender detection methods based on handwriting analysis
Neural computing & applications, Vol.35, pp.23909-23925
11/2023
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Abstract
Handwriting recognition and analysis has been an active area of research in the last two decades. Handwriting analysis is being studied in various fields of science, such as graphology, neurology, psychology, and computer science. Furthermore, automated handwriting analysis has several applications, including forensic, security, medical, and disease prediction. This paper presents the most recent handwriting analysis techniques and advancements available in the literature for age and gender classification/detection. Different steps, including feature extraction and classification, frequently used in the literature for age and gender detection, are discussed, and the presented works are classified according to the applied feature extraction and classification methods. The online and offline benchmark databases are also reviewed. We used a text mining technique to perform a quantitative content analysis of the presented research and better understand the co-occurrence network diagrams of age and gender classification/detection. This study is a valuable resource that provides new research directions to students and researchers interested in this field for further research and investigation.
Details
- Title
- Review of age and gender detection methods based on handwriting analysis
- Creators
- Fahimeh Alaei (Corresponding Author) - Southern Cross UniversityAlireza Reza Alaei (Contributor) - Southern Cross University
- Publication Details
- Neural computing & applications, Vol.35, pp.23909-23925
- Publisher
- Springer UK
- Identifiers
- 991013139013602368
- Copyright
- (c) The Author(s) 2023 This article is licensed under a Creative Commons Attribution 4.0 International License, (http://creativecommons.org/licenses/by/4.0/). Open Access funding enabled and organized by CAUL and its Member Institutions.
- Academic Unit
- Faculty of Business, Law and Arts; Information Technology
- Language
- English
- Resource Type
- Journal article