Head Pose Estimation Using Weighted Localized Gradient Orientation Histogram
Cui Wangl · Xi'an Jiaotong Daxue xuebao · 2015
When used for real-time 3D head-pose estimation,the facial features based on the localized gradient orientation histogram are easily affected by the environment and background so that the detection accuracy cannot meet the practical requirements.To reduce the influence of environment and background in images and video sequences,this paper presents a new weighted localized gradient orientation histogram to represent the facial features.During the computation,faces are detected and made the same size firstly.The gradient orientations of every point in the facial area are computed and then weighted by its skin-color probability and a Gaussian random value.Based on these gradient orientations a weighted localized gradient orientation histogram is obtained,in which the role of facial area is increased and that of environment and background are reduced.Finally the relationship between the 3D head-pose and the new features is computed using nonlinear support vector regression method.The results of numerical experiments show that this new method has a reletively high detection accuracy.