Continuous Head Pose Estimation Using Manifold Subspace Embedding and Multivariate Regression

Katerine Diaz-Chito, Jesus Martinez Del Rincon, Aura Hernandez-Sabate, Debora Gil

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)


© 2013 IEEE. In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learning-based methods, due to their promising generalization properties shown for face modeling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors, and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face data sets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-the-art methods, with angular errors varying between 9° and 17°.
Original languageEnglish
Pages (from-to)18325-18334
JournalIEEE Access
Publication statusPublished - 17 Mar 2018


  • B-splines
  • HOG features
  • Head pose estimation
  • generalized discriminative common vectors
  • multiple linear regression


Dive into the research topics of 'Continuous Head Pose Estimation Using Manifold Subspace Embedding and Multivariate Regression'. Together they form a unique fingerprint.

Cite this