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 CNN model which was pre-trained 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, while on the unconstrained UTKFace dataset, we achieved 4.867 of 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 group classification task on Adience dataset and our model achieved an accuracy of 61.4%.