{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T02:44:33Z","timestamp":1775616273283,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Automatic age estimation from facial images is an exciting machine learning topic that has attracted researchers\u2019 attention over the past several years. Numerous human\u2013computer 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.<\/jats:p>","DOI":"10.3390\/bdcc6040128","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:17:48Z","timestamp":1666768668000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Facial Age Estimation Using Machine Learning Techniques: An Overview"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7545-1605","authenticated-orcid":false,"given":"Khaled","family":"ELKarazle","sequence":"first","affiliation":[{"name":"School of Information and Communication Technologies, Swinburne University of Technology (Sarawak Campus), Sarawak 93350, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9363-2319","authenticated-orcid":false,"given":"Valliappan","family":"Raman","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore 641014, India"}]},{"given":"Patrick","family":"Then","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technologies, Swinburne University of Technology (Sarawak Campus), Sarawak 93350, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S4","DOI":"10.1016\/j.asj.2005.09.012","article-title":"The anatomy of the aging face: Volume loss and changes in 3-dimensional topography","volume":"26","author":"Coleman","year":"2006","journal-title":"Aesthetic Surg. 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