Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes into account the majority of the challenges faced by existing methods of age estimate. Making use of the data set that serves as the foundation for the face estimation system in this region (IMDB-WIKI). By performing preparatory processing activities to setup and train the data in order to collect cases, and by using the CNN deep learning method, which yielded results with an accuracy of 0.960 percent, we were able to reach our objective.
