Gender and age groups classifications for semantic annotation of videos

Abstract

This paper presents a combination of methods for gender identification and age group classification for semantic annotation of videos. The system has two different running modes as 'Training Mode' and 'Classification Mode'. The gender classifier achieves over 96% accuracy and the age group classifier achieves over 87% accuracy in age group classification. © 2011 Springer Science+Business Media B.V.