Demographic classification from face videos using manifold learning

Research on automatic demographic classification is still in its infancy despite the vast potential applications. The few existing works are only based on static images while nowadays input data in many real-world applications consist of video sequences. From these observations and also inspired by studies in neuroscience emphasizing manifold ways of visual perception, we propose in this work a novel approach to demographic classification from video sequences which encodes and exploits the correlation between the face images through manifold learning. Our extensive experiments on the gender and age classification problems show that the proposed manifold learning based approach yields in excellent results outperforming those of traditional static image based methods. Furthermore, to gain insight into the proposed approach, we also investigate an LBP (local binary patterns) based spatiotemporal method as a baseline system for combining spatial and temporal information to demographic classification from videos.