Gender Estimation from a Hybrid of Face, Upper and Full Body Images at Varying Body Poses

High gender classification accuracies have been recorded with high-resolution faces under controlled conditions. However, real-life scenarios are faced with challenges not limited to high pose variations in subjects, poor visibility, occlusion, and distance from camera. These have led to the current trend in estimating gender from full body images, notwithstanding the challenges posed by partial body images in a typical life scenario. We demonstrate that there are certain sections in a body image, the face, upper or lower body that are useful for recognition at near or far distances. Given the challenges of body captured at far distance or partially showing body in a photo, we therefore propose a combination of three classifiers for gender estimation from face; upper and full body from single-shot image. Our results in far compared to near distance images suggest that gender is best estimated from a hybrid of face; upper and full body images under challenging conditions.