Quality Assessment Based Denoising to Improve Face Recognition Performance
Abstract
A probe face image may contain noise due to environmental conditions, incorrect use of sensors or transmission error. The performance of face recognition severely depletes when the probe image is contaminated with noise. Denoising techniques can improve recognition performance, provided the correct parameters are used. In this paper, a parameter selection framework is presented. In the proposed framework, the optimal parameter set is selected for denoising using quality assessment algorithms with low complexity. Quality score based parameter selection is evaluated on the AR face dataset. A correlation study is discussed to ascertain the relationship between the quality scores and recognition rate. The experiments suggest that the proposed framework improves the performance both in terms of accuracy and computation time.
Cite
Text
Bharadwaj et al. "Quality Assessment Based Denoising to Improve Face Recognition Performance." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2011. doi:10.1109/CVPRW.2011.5981843Markdown
[Bharadwaj et al. "Quality Assessment Based Denoising to Improve Face Recognition Performance." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2011.](https://mlanthology.org/cvprw/2011/bharadwaj2011cvprw-quality/) doi:10.1109/CVPRW.2011.5981843BibTeX
@inproceedings{bharadwaj2011cvprw-quality,
title = {{Quality Assessment Based Denoising to Improve Face Recognition Performance}},
author = {Bharadwaj, Samarth and Bhatt, Himanshu S. and Vatsa, Mayank and Singh, Richa and Noore, Afzel},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2011},
pages = {140-145},
doi = {10.1109/CVPRW.2011.5981843},
url = {https://mlanthology.org/cvprw/2011/bharadwaj2011cvprw-quality/}
}