Pose estimation via complex-frequency domain analysis of image gradient orientations

Head Pose Estimation (HPE) has recently attracted a lot of interests in various computer vision applications. One challenging problem for accurate HPE is to model the intrinsic variations among poses, and suppress the extraneous variations derived from other factors, such as the illumination changes, outliers, and noise. To this end, this paper proposes a simple and efficient facial description for head pose estimation from images. To handle the illumination changes, we characterize each image pixel by its image gradient orientation (IGO), rather than the intensity, which is sensitive to illumination changes. We then carry out complex-frequency domain analysis of the IGO image via the two-dimensional image transform, such as the 2D Discrete Cosine Transform (DCT2), to encode the spatial configuration of image gradient orientations. The proposed facial description is called IGO-DCT2. It is robust to illumination changes, outliers, and noise. In addition, it is learning free and computationally efficient. Finally, the fine-grain head pose estimation is regarded as a regression problem and off-the-shelf non-linear regression models are used to learn the mapping from the feature space to the continuous pose labels. Experimental results show the proposed facial description achieves highly competitive results on the publicly available FacePix dataset.