[1]张毅,廖巧珍,罗元.融合二阶HOG与CS-LBP的头部姿态估计[J].智能系统学报编辑部,2015,10(5):741-746.[doi:10.11992/tis.201506019]
ZHANG Yi,LIAO Qiaozhen,LUO Yuan.Head pose estimation fusing the second order HOG and CS-LBP[J].CAAI Transactions on Intelligent Systems,2015,10(5):741-746.[doi:10.11992/tis.201506019]
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ZHANG Yi,LIAO Qiaozhen,LUO Yuan.Head pose estimation fusing the second order HOG and CS-LBP[J].CAAI Transactions on Intelligent Systems,2015,10(5):741-746.[doi:10.11992/tis.201506019]
融合二阶HOG与CS-LBP的头部姿态估计
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
10
期数:
2015年第5期
页码:
741-746
栏目:
学术论文—机器感知与模式识别
出版日期:
2015-10-25
- Title:
- Head pose estimation fusing the second order HOG and CS-LBP
- Keywords:
- head pose estimation; histogram of the orientation gradient (HOG); center symmetric local binary pattern (CS-LBP); kernel principal component analysis (KPCA); support vector machine (SVM)
- 分类号:
- TP391.4
- 文献标志码:
- A
- 摘要:
- 针对头部姿态估计受光照变化、表情、噪声干扰等因素影响导致识别率低的问题,提出一种融合二阶梯度方向直方图(HOG)和中心对称局部二值模式(CS-LBP)特征的姿态特征,用于单帧图像的头部姿态估计。采用二阶HOG对人脸图像进行形状信息提取,得到人脸的轮廓特征;用CS-LBP进行局部纹理信息的提取,通过将二阶HOG提取的轮廓特征和CS-LBP提取的纹理特征进行融合,得到更有效的人脸特征;将融合的姿态特征通过核主成分分析(KPCA)变换非线性映射到高维核空间中,抽取其主元特征分量,采用支持向量机(SVM)分类器进行姿态估计。实验结果表明,方法和HOG、LBP、二阶HOG、CS-LBP方法相比有更高的分类准确率,对光照的变化有很好的鲁棒性。
- Abstract:
- In order to improve head pose recognition rate under variable illumination, expression, and noise, etc., a novel pose feature, fusing the second order histogram of the orientation gradient (HOG) with the center symmetric local binary pattern (CS-LBP) feature, is proposed in order to estimate head pose in a single frame image. The contour information of the facial image is extracted by the second order HOG, deriving the facial contour feature. CS-LBP is used to extract local texture information. More effective facial features can be obtained by fusing contour feature extracted by the second order HOG and the texture feature extracted by CS-LBP. Kernel principal component analysis (KPCA) is used to nonlinearly project the fused pose feature into a higher dimensional kernel space so as to further select the primary feature. A support vector machine (SVM) classifier is used for pose estimation. Experiment results show that the proposed method is more accurate than the HOG method and the LBP method. This method has good robustness for variable illumination.
- 参考文献/References:
-
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备注/Memo
收稿日期:2015-06-11;改回日期:。
基金项目:国家自然科学基金资助项目(60905066).
作者简介:张毅, 男,1970年生,教授,博士生导师,主要研究方向为智能系统与移动机器人、机器人自主导航、机器视觉与模式识别、多传感器信息融合。主持并完成省部级及其他科研项目10余项,申请国家发明专利4项。发表论文60余篇,其中被SCI、EI、ISTP收录30余篇,出版专著1部,教材2部;廖巧珍,女,1989年生,硕士研究生,主要研究方向为模式识别和人机交互;罗元,女,1972年生,博士,教授,主要研究方向为机器人视觉导航、图像处理与模式识别。主持国家自然科学基金、重庆市自然科学基金、重庆市LED重大专项等国家和省部级科研项目10余项,获重庆市科技进步三等奖1项,申请和获得国家发明专利20余项。发表学术论文60余篇,其中被SCI、EI检索30余篇,出版专著4部。
通讯作者:廖巧珍.E-mail:490957008@qq.com.
更新日期/Last Update:
2015-11-16