AGE ESTIMATION USING SPECIFIC DOMAIN TRANSFER LEARNING

Abstract

Nowadays, the engagement of deep neural networks in computer vision increases the ability to achieve higher accuracy in many learning tasks such as face recognition and detection. However, the automatic estimation of human age is still considered as the most challenging facial task that demands extra efforts to obtain an accepted accuracy for real application. In this paper, we attempt to obtain a satisfied model that overcomes the overfitting problem, by fine-tuning CNNs model which was pretrained on face recognition task to estimate the real age. To make the model more robust, we evaluated the model for real age estimation on two types of datasets: on the constrained FG_NET dataset we achieved 3.446 of MAE, on the unconstrained UTKFace dataset we achieved 4.867 MAE. The experimental results of our approach outperform other state-of-the-art age estimation models on the benchmark datasets. We also fine-tuned the model for age groups classification task on Adience dataset, and our model achieved an accuracy of 61.4.