Estimating head pose orientation using extremely low resolution images

The estimation of human head pose is of interest in some surveillance and human-computer interaction scenarios. Traditionally, this is not a difficult task if high- or even standard-definition video cameras are used. However, such cameras cannot be used in scenarios requiring privacy protection. In this paper, we propose a non-linear regression method for the estimation of human head pose from extremely low resolution images captured by a monocular RGB camera. We evaluate the common histogram of oriented gradients (HoG) feature, propose a new gradient-based feature, and use Support Vector Regression (SVR) to estimate head pose. We evaluate our algorithm on the Biwi Kinect Head Pose Dataset by re-sizing full-resolution RGB images to extremely low resolutions. The results are promising. At 10×10-pixel resolution, we achieve 6.95, 9.92 and 12.88 degree mean-absolute errors (MAE) for roll, yaw and pitch angles, respectively. These errors are very close to state-of-the-art results for full-resolution images.