Gender Classification Using Pyramid Segmentation for Unconstrained Back-facingVideo Sequences

This paper presents a pioneering study on gender classification from unconstrained back-facing video sequences in natural scenes. In many cases, classifying gender simply via faces or other biometric cues may fail when the video only contains back-facing people. To address this problem, we propose a novel approach to classify the gender according to back-facing video sequences. For this task, a novel Pyramid Segmentation approach is proposed to divide video sequence into a suite of equal time-length sleeves with different scales. Moreover, a heuristic approach is used to compute weights for different features from each sleeve. Finally, a framework of gender classification based on video sequences is presented. To validate our approach, we introduce a new dataset, called BackFacing dataset, featured by 720 annotated back-facing human video sequences. To our knowledge, this is the first dataset only containing back-facing video shots. Experiments demonstrate that the proposed approach achieves competitive results on VidTIMIT, CohnKanade, CASIA Gait and BackFacing datasets.