Comparison of Sparse Image Descriptors for Eyes Detection in Thermal Images

Mateusz Knapik, Bogusław Cyganek

2019

Abstract

Eye detection and localization are basic steps in many computer systems aimed at human fatigue monitoring. In this paper we evaluate performance of two sparse image descriptors for eye detection in the long-range IR spectrum. In the training phase, sparse descriptors of the training images are computed and used to create features vocabulary. Final detections are done using bag-of-words approach and additional heuristic for geometric constraints. Several thermal video sequences were recorded to allow for quantitive analysis of this approach. Experimental results show that our method achieves high accuracy in real conditions.

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Paper Citation


in Harvard Style

Knapik M. and Cyganek B. (2019). Comparison of Sparse Image Descriptors for Eyes Detection in Thermal Images. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 638-644. DOI: 10.5220/0007576506380644


in Bibtex Style

@conference{visapp19,
author={Mateusz Knapik and Bogusław Cyganek},
title={Comparison of Sparse Image Descriptors for Eyes Detection in Thermal Images},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={638-644},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007576506380644},
isbn={978-989-758-354-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Comparison of Sparse Image Descriptors for Eyes Detection in Thermal Images
SN - 978-989-758-354-4
AU - Knapik M.
AU - Cyganek B.
PY - 2019
SP - 638
EP - 644
DO - 10.5220/0007576506380644
PB - SciTePress