数据与计算发展前沿2026，Vol.8Issue(1)：129-147,19.DOI:10.11871/jfdc.issn.2096-742X.2026.01.011
基于笔迹的书写者性别与年龄检测研究综述
Review of Research on Gender and Age Detection of Writers Based on Handwriting
摘要
Abstract
[Objective]The purpose of this paper is to systematically review the current research status and future trends in handwriting-based gender and age detection of writers.[Methods]The re-view first outlines the main datasets and application scenarios,then categorizes handwriting rec-ognition models into traditional machine learning and deep learning methods.For traditional methods,the characteristics of algorithms such as SVM,KNN,and decision trees are analyzed.For deep learning methods,the analysis is divided into end-to-end neural networks and feature extraction networks.The advantages and disadvantages of different methods are evaluated by comparing their per-formance on identical datasets.[Results]This paper comprehensively summarizes the research status of gender and age detection technology based on handwriting,and conducts an in-depth analysis of existing models and methods.Research shows that deep learning models have significant advantages in feature extraction and classifi-cation accuracy,while traditional machine learning methods maintain unique advantages when processing small-scale datasets.Current research faces challenges,including the lack of public Chinese datasets,insufficient model interpretability,and low accuracy in fine-grained age classification.Future research should focus on developing multilingual datasets,innovating visual model architectures,deepening attention mechanism applications,and ad-vancing multimodal feature fusion,promoting the practical application of handwriting recognition technology in high-reliability scenarios.关键词
笔迹识别/性别检测/年龄分类/机器学习/深度学习Key words
handwriting recognition/gender detection/age classification/machine learning/deep learning引用本文复制引用
蔡毅,王晓宾,陈蕊丽,韩珣..基于笔迹的书写者性别与年龄检测研究综述[J].数据与计算发展前沿,2026,8(1):129-147,19.基金项目
智能警务四川省重点实验室开放课题资助(ZNJW2023KFMS007) （ZNJW2023KFMS007）
中国人民公安大学刑事科学技术双一流创新研究专项(2023SYL06) （2023SYL06）
