Head Pose Estimation with Improved Random Regression Forests

被引:0
|
作者
Sang, Gaoli [1 ]
Chen, Hu [1 ]
Zhao, Qijun [1 ]
机构
[1] Sichuan Univ, Coll Comp Sci, State Key Lab Fundamental Sci Synthet Vis, Chengdu 610064, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
D O I
10.1155/2015/703514
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Perception of head pose is useful for many face-related tasks such as face recognition, gaze estimation, and emotion analysis. In this paper, we propose a novel random forest based method for estimating head pose angles from single face images. In order to improve the effectiveness of the constructed head pose predictor, we introduce feature weighting and tree screening into the random forest training process. In this way, the features with more discriminative power are more likely to be chosen for constructing trees, and each of the trees in the obtained random forest usually has high pose estimation accuracy, while the diversity or generalization ability of the forest is not deteriorated. The proposed method has been evaluated on four public databases as well as a surveillance dataset collected by ourselves. The results show that the proposed method can achieve state-of-the-art pose estimation accuracy. Moreover, we investigate the impact of pose angle sampling intervals and heterogeneous face images on the effectiveness of trained head pose predictors.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Real Time Head Pose Estimation with Random Regression Forests
    Fanelli, Gabriele
    Gall, Juergen
    Van Gool, Luc
    [J]. 2011 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2011, : 617 - 624
  • [2] Dynamic random regression forests for real-time head pose estimation
    Ying, Ying
    Wang, Han
    [J]. MACHINE VISION AND APPLICATIONS, 2013, 24 (08) : 1705 - 1719
  • [3] Dynamic random regression forests for real-time head pose estimation
    Ying Ying
    Han Wang
    [J]. Machine Vision and Applications, 2013, 24 : 1705 - 1719
  • [4] Real-Time Head Pose Estimation Using Random Regression Forests
    Tang, Yunqi
    Sun, Zhenan
    Tan, Tieniu
    [J]. BIOMETRIC RECOGNITION: CCBR 2011, 2011, 7098 : 66 - 73
  • [5] Head pose Estimation via Direction-sensitive Feature and Random Regression Forests
    Liu, Jianming
    Zeng, Jiguo
    [J]. COMPUTER AND INFORMATION TECHNOLOGY, 2014, 519-520 : 693 - 702
  • [6] Fast and Accurate Head Pose Estimation via Random Projection Forests
    Lee, Donghoon
    Yang, Ming-Hsuan
    Oh, Songhwai
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, : 1958 - 1966
  • [7] Real-Time Head Pose Estimation Using Weighted Random Forests
    Kim, Hyunduk
    Sohn, Myoung-Kyu
    Kim, Dong-Ju
    Ryu, Nuri
    [J]. COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, ICCCI 2014, 2014, 8733 : 554 - 562
  • [8] Head pose estimation by regression algorithm
    Abate, Andrea F.
    Barra, Paola
    Pero, Chiara
    Tucci, Maurizio
    [J]. PATTERN RECOGNITION LETTERS, 2020, 140 : 179 - 185
  • [9] Head Pose Estimation: Classification or Regression?
    Guo, Guodong
    Fu, Yun
    Dyer, Charles R.
    Huang, Thomas S.
    [J]. 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, : 567 - +
  • [10] Metric Regression Forests for Human Pose Estimation
    Pons-Moll, Gerard
    Taylor, Jonathan
    Shotton, Jamie
    Hertzmann, Aaron
    Fitzgibbon, Andrew
    [J]. PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2013, 2013,