Synergistic Face Detection and Pose Estimation with Energy-Based Models
Abstract
We describe a novel method for simultaneously detecting faces and estimating their pose in real time. The method employs a convolutional network to map images of faces to points on a low-dimensional manifold parametrized by pose, and images of non-faces to points far away from that manifold. Given an image, detecting a face and estimating its pose is viewed as minimizing an energy function with respect to the face/non-face binary variable and the continuous pose parameters. The system is trained to minimize a loss function that drives correct combinations of labels and pose to be associated with lower energy values than incorrect ones.
Cite
Text
Osadchy et al. "Synergistic Face Detection and Pose Estimation with Energy-Based Models." Journal of Machine Learning Research, 2007.Markdown
[Osadchy et al. "Synergistic Face Detection and Pose Estimation with Energy-Based Models." Journal of Machine Learning Research, 2007.](https://mlanthology.org/jmlr/2007/osadchy2007jmlr-synergistic/)BibTeX
@article{osadchy2007jmlr-synergistic,
title = {{Synergistic Face Detection and Pose Estimation with Energy-Based Models}},
author = {Osadchy, Margarita and Le Cun, Yann and Miller, Matthew L.},
journal = {Journal of Machine Learning Research},
year = {2007},
pages = {1197-1215},
volume = {8},
url = {https://mlanthology.org/jmlr/2007/osadchy2007jmlr-synergistic/}
}