Recently, vast attention has grown in the field of computer vision, especially in face recognition, detection, and facial landmarks localization. Many significant features can be directly derived from the human face, such as age, gender, and race. Estimating the age can be defined as the automatic process of classifying the facial image into the exact age or to a specific age range. Practically, age estimation from the face is still a challenging problem due to the effects from many internal factors, such as gender and race, and external factors, such as environments and lifestyle. Huge efforts have been addressed to reach an accepted and satisfied accuracy of age estimation task. In this paper, we try to analyze the main aspects that can increase the performance of the age estimation system, present the handcrafted-based models and deep learning-based models, and show how the evaluations are being conducted, discuss the proposed algorithms and models in the age estimation, and show the main limitations and challenges facing the age estimation process. Also, different aging databases that contain age annotations are discussed. Finally, few guidelines and the future prospect related to the age estimation are investigated.