CSEye: A Proposed Solution for Accurate and Accessible One-to-Many Face Verification
Abstract
Facial verification is a core problem studied by researchers in computer vision. Recently published one-to-one comparison models have successfully achieved accuracy results that surpass the abilities of humans. A natural extension to the one-to-one facial verification problem is a one-to-many classification. In this abstract, we present our exploration of different methods of performing one-to-many facial verification using low-resolution images. The CSEye model introduces a direct comparison between the features extracted from each of the candidate images and the suspect before performing the classification task. Initial experiments using 10-to-1 comparisons of faces from the Labelled Faces of the Wild dataset yield promising results.
Cite
Text
Dharamshi and Zou. "CSEye: A Proposed Solution for Accurate and Accessible One-to-Many Face Verification." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019933Markdown
[Dharamshi and Zou. "CSEye: A Proposed Solution for Accurate and Accessible One-to-Many Face Verification." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/dharamshi2019aaai-cseye/) doi:10.1609/AAAI.V33I01.33019933BibTeX
@inproceedings{dharamshi2019aaai-cseye,
title = {{CSEye: A Proposed Solution for Accurate and Accessible One-to-Many Face Verification}},
author = {Dharamshi, Ameer and Zou, Rosie Yuyan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {9933-9934},
doi = {10.1609/AAAI.V33I01.33019933},
url = {https://mlanthology.org/aaai/2019/dharamshi2019aaai-cseye/}
}