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 journalArticlepeer-review

10 Citations (Scopus)
201 Downloads (Pure)


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 modelling 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 datasets, 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-art methods, with angular errors varying between 9 and 17 degree
Original languageEnglish
Pages (from-to)18325-18334
Number of pages10
JournalIEEE Access
Issue number1
Publication statusPublished - 19 Mar 2018

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