Biternion Nets: Continuous Head Pose Regression from Discrete Training Labels

Lucas Beyer, Alexander Hermans, Bastian Leibe
German Conference on Pattern Recognition (GCPR'15) - Oral

TL;DR: By doing the obvious thing of encoding an angle φ as (cos φ, sin φ), we can do cool things and simplify data labeling requirements.

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° 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.


» Show BibTeX
@inproceedings{Beyer2015BiternionNets, author = {Lucas Beyer and Alexander Hermans and Bastian Leibe}, title = {Biternion Nets: Continuous Head Pose Regression from Discrete Training Labels}, booktitle = {Pattern Recognition}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = {9358}, pages = {157-168}, year = {2015}, isbn = {978-3-319-24946-9}, doi = {10.1007/978-3-319-24947-6_13}, ee = {http://lucasb.eyer.be/academic/biternions/biternions_gcpr15.pdf}, }



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