Rotating Your Face Using Multi-Task Deep Neural Network
Abstract
Face recognition under viewpoint and illumination changes is a difficult problem, so many researchers have tried to solve this problem by producing the pose- and illumination- invariant feature. Zhu et al. [26] changed all arbitrary pose and illumination images to the frontal view image to use for the invariant feature. In this scheme, preserving identity while rotating pose image is a crucial issue. This paper proposes a new deep architecture based on a novel type of multitask learning, which can achieve superior performance in rotating to a target-pose face image from an arbitrary pose and illumination image while preserving identity. The target pose can be controlled by the user's intention. This novel type of multi-task model significantly improves identity preservation over the single task model. By using all the synthesized controlled pose images, called Controlled Pose Image (CPI), for the pose- illumination- invariant feature and voting among the multiple face recognition results, we clearly outperform the state-of-the-art algorithms by more than 4~6% on the MultiPIE dataset.
Cite
Text
Yim et al. "Rotating Your Face Using Multi-Task Deep Neural Network." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298667Markdown
[Yim et al. "Rotating Your Face Using Multi-Task Deep Neural Network." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/yim2015cvpr-rotating/) doi:10.1109/CVPR.2015.7298667BibTeX
@inproceedings{yim2015cvpr-rotating,
title = {{Rotating Your Face Using Multi-Task Deep Neural Network}},
author = {Yim, Junho and Jung, Heechul and Yoo, ByungIn and Choi, Changkyu and Park, Dusik and Kim, Junmo},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2015},
doi = {10.1109/CVPR.2015.7298667},
url = {https://mlanthology.org/cvpr/2015/yim2015cvpr-rotating/}
}