Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition
Abstract
Heterogeneous face recognition is the problem of identifying a person from a face image acquired with a nontraditional sensor by matching it to a visible gallery. Most approaches to this problem involve modeling the relationship between corresponding images from the visible and sensing domains. This is typically done at the patch level and/or with shallow models with the aim to prevent overfitting. In this work, rather than modeling local patches or using a simple model, we propose to use a complex, deep model to learn the relationship between the entirety of cross-modal face images. We describe a deep convolutional neural network based method that leverages a large visible image face dataset to prevent overfitting. We present experimental results on two benchmark datasets showing its effectiveness.
Cite
Text
Reale et al. "Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016. doi:10.1109/CVPRW.2016.47Markdown
[Reale et al. "Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2016.](https://mlanthology.org/cvprw/2016/reale2016cvprw-seeing/) doi:10.1109/CVPRW.2016.47BibTeX
@inproceedings{reale2016cvprw-seeing,
title = {{Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition}},
author = {Reale, Christopher and Nasrabadi, Nasser M. and Kwon, Heesung and Chellappa, Rama},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2016},
pages = {320-328},
doi = {10.1109/CVPRW.2016.47},
url = {https://mlanthology.org/cvprw/2016/reale2016cvprw-seeing/}
}