Gait Energy Image Reconstruction from Degraded Gait Cycle Using Deep Learning
Abstract
Gait energy image (GEI) is considered as an effective gait representation for gait-based human identification. In gait recognition, normally, GEI is computed from one full gait cycle. However in many circumstances, such a full gait cycle might not be available due to occlusion. Thus, the GEI is not complete, giving a rise to degrading gait identification rate. In this paper, we address this issue by proposing a novel method to reconstruct a complete GEI from a few frames of gait cycle. To do so, we propose a deep learning-based approach to transform incomplete GEI to the corresponding complete GEI obtained from a full gait cycle. More precisely, this transformation is done gradually by training several fully convolutional networks independently and then combining these as a uniform model. Experimental results on a large public gait dataset, namely OULP demonstrate the validity of the proposed method for gait identification when dealing with very incomplete gait cycles.
Cite
Text
Babaee et al. "Gait Energy Image Reconstruction from Degraded Gait Cycle Using Deep Learning." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11018-5_52Markdown
[Babaee et al. "Gait Energy Image Reconstruction from Degraded Gait Cycle Using Deep Learning." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/babaee2018eccvw-gait/) doi:10.1007/978-3-030-11018-5_52BibTeX
@inproceedings{babaee2018eccvw-gait,
title = {{Gait Energy Image Reconstruction from Degraded Gait Cycle Using Deep Learning}},
author = {Babaee, Maryam and Li, Linwei and Rigoll, Gerhard},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {654-658},
doi = {10.1007/978-3-030-11018-5_52},
url = {https://mlanthology.org/eccvw/2018/babaee2018eccvw-gait/}
}