Agbo-Ajala Olatunbosun, Viriri Serestina, Oloko-Oba Mustapha, Ekundayo Olufisayo, Heymann Reolyn
Computer Science Discipline, School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa.
Front Big Data. 2022 Oct 26;5:1025806. doi: 10.3389/fdata.2022.1025806. eCollection 2022.
Apparent age estimation human face image has attracted increased attention due to its numerous real-world applications. Predicting the apparent age has been quite difficult for machines and humans. However, researchers have focused on machine estimation of "age as perceived" to a high level of accuracy. To further improve the performance of apparent age estimation from the facial image, researchers continue to examine different methods to enhance its results further. This paper presents a critical review of the modern approaches and techniques for the apparent age estimation task. We also present a comparative analysis of the performance of some of those approaches on the apparent facial aging benchmark. 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. 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. 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.
由于其在众多现实世界中的应用，人脸图像的表观年龄估计已引起越来越多的关注。对机器和人类来说，预测表观年龄一直相当困难。然而，研究人员已将重点放在将“感知年龄”的机器估计提高到高精度水平上。为了进一步提高从面部图像进行表观年龄估计的性能，研究人员继续研究不同的方法以进一步提升其结果。本文对表观年龄估计任务的现代方法和技术进行了批判性综述。我们还对其中一些方法在表观面部老化基准上的性能进行了比较分析。该研究还突出了用于表观年龄估计的每种方法的优缺点，以指导在该领域未来工作中选择合适的算法。这项工作聚焦于最流行的算法以及那些在表观年龄估计方面似乎最成功的算法，以改进现有的最先进结果。我们基于三个面部老化数据集进行评估，包括“看脸”（LAP）-2015、LAP-2016和APPA-REAL，这是表观年龄估计最流行且公开可用的数据集基准。
