A Study of Two Image Representations for Head Pose Estimation
Traditional appearance-based head pose estimation methods use the holistic face appearance as the input and then employ subspace analysis methods to extract low-dimensional features for classification. However, the face appearance may be more related to the unique identity of an individual rather than head poses. In this paper, we presented a comparative study of two image representations which aim to specifically describe bead pose variations. The histogram of oriented gradient (HOG) based method relies on the gradient orientation distribution. The GaFour method exploits asymmetry in the intensities of each row of the face image, using a Gabor filter and Fourier transform to represent the face images. We compare the two image representations combined with two linear subspace methods (PCA and LDA). Experiments on two public face databases (CMU-PIE and CAS-PEAL) show that both HOG+LDA and GaFour+LDA give good results and HOG+LDA provides the best performance with a lower feature dimension.
Head pose estimation histogram of oriented gradient the GaFour feature
Ligeng Dong Linmi Tao Guangyou Xu Patrick Oliver
Dept. of Computer Science and Technology, Tsinghua University, Beijing, China Culture Lab, Computing Science, Newcastle University, Newcastle Upon Tyne, UK
国际会议
The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)
西安
英文
963-968
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）