Gender & Age Detection using CNN and Deep Learning

Abstract

An application for video data analysis based on computer vision and machine learning method are presented. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. As features for the gender and age estimation, facial shape, skin texture, hue and Gabor feature are used. In order to show the effectiveness of proposed method, not only real-age database of facial image but also appearance-age database is employed. We also analyze the facial features characteristic to each age category and gender, and examine the difference feature of between the real-age and appearance-age in a facial area. Moreover, we examined the left-right symmetric property of the face concerning gender and age estimation by the proposed method. The promising practical application of such algorithms can be human-computer interaction, surveillance monitoring, video content analysis, targeted advertising, biometrics, and entertainment.