Improving the Accuracy of Face Detection for Damaged Video and Distant Targets

Jun-Horng Chen

2014

Abstract

This work aims at improving the accuracy of face detection in two scenarios, when the video quality is deteriorated by the transmission link and when the target is far away from the camera. In block based coding, the packet loss inevitably makes the corrupted face image lacks some blocks. This work proposes the sparse modeling error concealment can coarsely recover the lost blocks, the fine texture can be obtained by diminishing the edge discontinuity, and a satisfied result for face detection can thus be recovered. Furthermore, this work utilizes the relationship learning super-resolution method to enhance the resolution in the case of face image taken from a long distance. Experimental results demonstrate that the proposed approach can effectively increase the accuracy of face detection for severely degraded and low resolution face images.

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Paper Citation


in Harvard Style

Chen J. (2014). Improving the Accuracy of Face Detection for Damaged Video and Distant Targets . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 351-355. DOI: 10.5220/0005161603510355


in Bibtex Style

@conference{ncta14,
author={Jun-Horng Chen},
title={Improving the Accuracy of Face Detection for Damaged Video and Distant Targets},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={351-355},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005161603510355},
isbn={978-989-758-054-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Improving the Accuracy of Face Detection for Damaged Video and Distant Targets
SN - 978-989-758-054-3
AU - Chen J.
PY - 2014
SP - 351
EP - 355
DO - 10.5220/0005161603510355