Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions

@article{Drouard2016RobustHE,
  title={Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions},
  author={Vincent Drouard and Radu Horaud and Antoine Deleforge and Sil{\`e}ye O. Ba and Georgios Evangelidis},
  journal={IEEE Transactions on Image Processing},
  year={2016},
  volume={26},
  pages={1428-1440}
}
  • Vincent DrouardR. Horaud Georgios Evangelidis
  • Published 31 March 2016
  • Computer Science
  • IEEE Transactions on Image Processing
Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose to use a mixture of linear regressions with partially-latent output. This regression method… 
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