There has been an increasing interest in age estimation from facial images (Drobnyh and Polovinkin, 2017) due to its increasing demands in various potential applications in security control (Abbas and Kareem, 2018), human-computer interaction (Abbas and Kareem, 2018), social media (Ruiz-Del-Solar et al., 2009), and forensic studies (Bouchrika et al., 2016). We based our evaluations on three facial aging datasets, including looking at people (LAP)-2015, LAP-2016, and APPA-REAL, the most popular and publicly available datasets benchmark for apparent age estimation.Īge estimation is a very prolific area of research within the computer vision community (Huerta et al., 2015 Drobnyh and Polovinkin, 2017). The work focuses on the most popular algorithms and those that appear to have been the most successful for apparent age estimation to improve on the existing state-of-the-art results. The study also highlights the strengths and weaknesses of each approach used for apparent age estimation to guide in choosing the appropriate algorithms for future work in the field. We also present a comparative analysis of the performance of some of those approaches on the apparent facial aging benchmark. This paper presents a critical review of the modern approaches and techniques for the apparent age estimation task. To further improve the performance of apparent age estimation from the facial image, researchers continue to examine different methods to enhance its results further. However, researchers have focused on machine estimation of “age as perceived” to a high level of accuracy. Predicting the apparent age has been quite difficult for machines and humans. Apparent age estimation via human face image has attracted increased attention due to its numerous real-world applications.
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