Automatic head pose estimation using randomly projected dense SIFT descriptors
Huy Tho Ho, Rama Chellappa · 2012
In this paper, we propose an automatic method for determining the head pose from a given face image. The face image is divided into a regular grid and a representation of the image is obtained by extracting dense SIFT descriptors from its grid points. Random Projection (RP) is then applied to reduce the dimension of the concatenated SIFT descriptor vector. Classification and regression using Support Vector Machine (SVM) are combined in order to obtain an accurate estimate of the head pose. The advantage of the proposed approach is that it does not require facial feature points such as eye corners, mouth corners and the nose tip to be extracted from the input face image as in many other methods. Experimental results are presented to demonstrate the effectiveness of the approach.