Face Recognition from Face Motion Manifolds Using Robust Kernel Resistor-Average Distance
Abstract
In this work we consider face recognition from face motion manifolds. An information-theoretic approach with Resistor-Average Distance (RAD) as a dissimilarity measure between distributions of face images is proposed. We introduce a kernel-based algorithm that retains the simplicity of the closed-form expression for the RAD between two normal distributions, while allowing for modelling of complex, nonlinear manifolds. Additionally, it is shown how errors in the face registration process can be modelled to significantly improve recognition. Recognition performance of our method is experimentally demonstrated and shown to outperform state-of-the-art algorithms. Recognition rates of 97-100% are consistently achieved on databases of 35-90 people.
Cite
Text
Arandjelovic and Cipolla. "Face Recognition from Face Motion Manifolds Using Robust Kernel Resistor-Average Distance." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2004. doi:10.1109/CVPR.2004.341Markdown
[Arandjelovic and Cipolla. "Face Recognition from Face Motion Manifolds Using Robust Kernel Resistor-Average Distance." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2004.](https://mlanthology.org/cvprw/2004/arandjelovic2004cvprw-face/) doi:10.1109/CVPR.2004.341BibTeX
@inproceedings{arandjelovic2004cvprw-face,
title = {{Face Recognition from Face Motion Manifolds Using Robust Kernel Resistor-Average Distance}},
author = {Arandjelovic, Ognjen and Cipolla, Roberto},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2004},
pages = {88},
doi = {10.1109/CVPR.2004.341},
url = {https://mlanthology.org/cvprw/2004/arandjelovic2004cvprw-face/}
}