Age and gender estimation by using hybrid facial features

Estimation of age and gender is one of the foremost challenges in computer vision and has lots of applications in recommender systems establishment, security and systems which deal with computer vision. In this paper, a method for age and gender estimation using facial images are proposed by focusing on the extraction of robust features existing in facial photos. The main stage for age estimation is done in two main steps. At the first step classification and extraction of the global features is done, and in the second step ratios which help distinguishing child (1 to 12 year-old children) from youth (13 to 40 year-old men) are described and in the next step by using the same procedure, seniors (from 41 to 80 years old) are separated from the two former groups. For gender estimation purpose, ratios computed in the previous step, are employed and finally the correct gender estimation is done.