Efficient Hand Detection on Client-server Recognition System

Victor Chernyshov

2015

Abstract

In this paper, an efficient method for hand detection based on continuous skeletons approach is presented. It showcased real-time working speed and high detection accuracy (3-5% both FAR and FRR) on a large dataset (50 persons, 80 videos, 2322 frames). This makes the method suitable for use as a part of modern hand biometrics systems including mobile ones. Next, the study shows that continuous skeletons approach can be used as prior for object and background color models in segmentation methods with supervised learning (e.g. interactive segmentation with seeds or abounding box). This fact was successfully adopted to the developed client-server hand recognition system — both thumbnailed colored frame and extracted seeds are sent from Android application to server where Grabcut segmentation is performed. As a result, more qualitative hand shape features are extracted which is confirmed by several identification experiments. Finally, it is demonstrated that hand detection results can be used as a region of interest localization routine in the subsequent analysis of finger knuckle print. The future research will be devoted to extracting features from dorsal fingers surface and developing multi-modal classifier (hand shape and knuckle print features) for identification problem.

Paper Citation

in Harvard Style

Chernyshov V. (2015). Efficient Hand Detection on Client-server Recognition System . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 461-468. DOI: 10.5220/0005315704610468

in Bibtex Style

@conference{visapp15,
author={Victor Chernyshov},
title={Efficient Hand Detection on Client-server Recognition System},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={461-468},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005315704610468},
isbn={978-989-758-090-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Efficient Hand Detection on Client-server Recognition System
SN - 978-989-758-090-1
AU - Chernyshov V.
PY - 2015
SP - 461
EP - 468
DO - 10.5220/0005315704610468