Facial Age Estimation Using Machine Learning Techniques: An Overview

ELKarazle Khaled; Raman Valliappan; Then Patrick

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Facial Age Estimation Using Machine Learning Techniques: An Overview

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摘要

Automatic age estimation from facial images is an exciting machine learning topic that has attracted researchers' attention over the past several years. Numerous human-computer interaction applications, such as targeted marketing, content access control, or soft-biometrics systems, employ age estimation models to carry out secondary tasks such as user filtering or identification. Despite the vast array of applications that could benefit from automatic age estimation, building an automatic age estimation system comes with issues such as data disparity, the unique ageing pattern of each individual, and facial photo quality. This paper provides a survey on the standard methods of building automatic age estimation models, the benchmark datasets for building these models, and some of the latest proposed pieces of literature that introduce new age estimation methods. Finally, we present and discuss the standard evaluation metrics used to assess age estimation models. In addition to the survey, we discuss the identified gaps in the reviewed literature and present recommendations for future research.

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《Big Data and Cognitive Computing》 |2022年第4期|共22页
作者
ELKarazle Khaled; Raman Valliappan; Then Patrick;
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作者单位

Swinburne Univ Technol;

Coimbatore Inst Technol;

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原文格式 PDF
正文语种英语
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关键词
automatic age estimation; age estimation review; deep learning; facial recognition; features extraction; image processing; CONVOLUTIONAL NEURAL-NETWORKS; GENDER ESTIMATION; TEXTURE; CLASSIFICATION;

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Facial Age Estimation Using Machine Learning Techniques: An Overview

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