A Survey on Deep Learning Face Age Estimation Model: Method and Ethnicity

Abstract

Face age estimation is a type of study in computer vision and pattern recognition. Designing an age estimation or classification model requires data as training samples for the machine to learn. Deep learning method has improved estimation accuracy and the number of deep learning age estimation models developed. Furthermore, numerous datasets availability is making the method an increasingly attractive approach. However, face age databases mostly have limited ethnic subjects, only one or two ethnicities and may result in ethnic bias during age estimation, thus impeding progress in understanding face age estimation. This paper reviewed available face age databases, deep learning age estimation models, and discussed issues related to ethnicity when estimating age. The review revealed changes in deep learning architectural designs from 2015 to 2020, frequently used face databases, and the number of different ethnicities considered. Although model performance has improved, the widespread use of specific few multi-races databases, such as the MORPH and FG-NET databases, suggests that most age estimation studies are biased against non-Caucasians/non-white subjects. Two primary reasons for face age research’s failure to further discover and understand ethnic traits effects on a person’s facial aging process: lack of multi-race databases and ethnic traits exclusion. Additionally, this study presented a framework for accounting ethnic in face age estimation research and several suggestions on collecting and expanding multi-race databases. The given framework and suggestions are also applicable for other secondary factors (e.g. gender) that affect face age progression and may help further improve future face age estimation research.