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Biternion Nets: Continuous Head Pose Regression from Discrete Training Labels

  • Lucas Beyer
  • Alexander Hermans
  • Bastian Leibe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

While head pose estimation has been studied for some time, continuous head pose estimation is still an open problem. Most approaches either cannot deal with the periodicity of angular data or require very fine-grained regression labels. We introduce biternion nets, a CNN-based approach that can be trained on very coarse regression labels and still estimate fully continuous \({360}^{\circ }\) head poses. We show state-of-the-art results on several publicly available datasets. Finally, we demonstrate how easy it is to record and annotate a new dataset with coarse orientation labels in order to obtain continuous head pose estimates using our biternion nets.

Supplementary material

371393_1_En_13_MOESM1_ESM.zip (1.5 mb)
Supplementary material 1 (zip 1526 KB)

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.Visual Computing InstituteRWTH Aachen UniversityAachenGermany

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