Gender and age recognition for video analytics solution

Abstract

An application for video data analysis based on computer vision and machine learning methods is presented. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 94% accuracy of viewer's gender recognition is achieved. Our age estimation algorithm provides world-quality results for MORTH database, but focused on real-life audience measurement videodata in which faces can be looks more or less similar to RUS-FD private database. In this case we can reach total mean absolute error score less than 7. All the video processing stages are united into a real-time system of audience analysis. The system allows to extract all the possible information about people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters. The promising practical application of such algorithms can be human-computer interaction, surveillance monitoring, video content analysis, targeted advertising, biometrics, and entertainment.