Gender Classification from Unconstrained Video Sequences

Abstract

This paper presents the first investigation into the classification of faces from unconstrained video sequences in natural scenes, i.e., with arbitrary poses, facial expressions, occlusions, illumination conditions and motion blur. To overcome difficulties from individual frames, a novel Bayesian formulation is proposed to estimate the posterior probability of a face trait at a specific time, conditional on features identified in previous frames of a video sequence. A Markov model is used to represent temporal dependencies, and classification involves determining the maximum a posteriori class at a given time. Showing the robustness of the proposed system, the Bayesian framework is first trained on a database collected under controlled conditions, and then applied to the previously unseen faces obtained from an unconstrained video database. The Markovian temporal model results in a gender classification rate of 90% by the last video frame, and is shown to outperform alternative approaches previously introduced in the literature.