Two Dimensional Partitioned Sparse Representation for Head Pose Estimation
Although classical sparse representation is capable to solve appearance based classification problems such as face recognition, it is problematic that images need to be converted to column vectors before subsequent processing which makes the computation expensive due to the huge dimension. From the human vision perspective, it is reasonable to observe image in form of matrix rather than vector. To reduce the computational complexity, the idea to partition the images is introduced as well. We combine partition processing with two dimensional sparse representation together to propose 2DPSRC (2D Partitioned Sparse Representation Classifier) considering the property of head pose estimation problem. It can greatly improve the estimation accuracy and enhance the efficiency of the computational process involved pursuit of Г1-norm minimization. Finally, experiments on Pointing’04 and Oriental Face Database show the effectiveness and robustness of our proposed method.
Chao ZHANG Yanning ZHANG Liang LIAO
Shaanxi Provincial Key Laboratory of Speech and Image Information ProcessingNorthwestern Polytechnic Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechni
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-5
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）