@InProceedings{MarcOliu2016,
author="Marc Oliu
and Ciprian Corneanu
and Laszlo A. Jeni
and Jeffrey F. Cohn
and Takeo Kanade
and Sergio Escalera",
title="Continuous Supervised Descent Method for Facial Landmark Localisation",
booktitle="13th Asian Conference on Computer Vision",
year="2016",
volume="10112",
pages="121--135",
abstract="Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method{\textquoteright}s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.",
optnote="HuPBA;MILAB;",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2838), last updated on Thu, 27 Apr 2023 13:18:46 +0200",
file=":http://refbase.cvc.uab.es/files/OCJ2016.pdf:PDF"
}