Age estimation based on face detection, is one of the different areas of study within digital images and is widely used by researchers and the by the different technological applications. Consequently, the present research focuses on studying the methods and the most important trends on age classification based on different analyzes of the main facial characteristics of a person such as the proportions of the face, identifying wrinkles, drooping eyelids, and fiducial points, among others. With these data, and using computational learning algorithms, better known as support vector machines and artificial neural networks, an analysis of some classification processes it is performed. Some age estimators such as OpenBR and Face API were also tried in order to obtain solutions that help generate new proposals for age estimation. Determining the age of people can be of assistance for marketing studies; for selecting contents suitable to certain age groups; for systems based on human interactions; and the probable detection of child pornography, which help to prevent criminal content subsisting on the web.