Head Pose Classification in Crowded Scenes

Abstract

We propose a novel technique for head pose classification in crowded public space under poor lighting and in low-resolution video images. Unlike previous approaches, we avoid the need for explicit segmentation of skin and hair regions from a head image and implicitly encode spatial information using a grid map for more robustness given lowresolution images. Specifically, a new head pose descriptor is formulated using similarity distance maps by indexing each pixel of a head image to the mean appearance templates of head images at different poses. These distance feature maps are then used to train a multi-class Support Vector Machine for pose classification. Our approach is evaluated against established techniques [3, 13, 14] using the i-LIDS underground scene dataset [9] under challenging lighting and viewing conditions. The results demonstrate that our model gives significant improvement in head pose estimation accuracy, with over 80% pose recognition rate against 32% from the best of existing models.