Development Study of Deep Learning Facial Age Estimation

Human age estimation is one of the most challenging problem because it can be used in many applications relating to age such as age-specific movies, age-specific computer applications or website, etc. This paper will contribute to give brief information about development of age estimation researches using deep learning. We explore three recent journal papers that give significant contribution in age estimation using deep learning. From these papers, they selected classification methods and there is gradual improvement in result and also in selected loss function. The best result gives MAE (mean average error) 2.8 years and VGG-16 is the most selected CNN architecture