Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild

Mohamed Selim, Suraj Sundararajan, Alain Pagani, Didier Stricker

2018

Abstract

Gender recognition is an important task in the field of facial image analysis. Gender can be detected using different visual cues, for example gait, physical appearance, and most importantly, the face. Deep learning has been dominating many classification tasks in the past few years. Gender classification is a binary classification problem, usually addressed using the facial image. In this work, we present a deep and compact CNN (GenderCNN) to estimate the gender from a facial image. We also, tackle the illumination and blurriness that appear in still images and appear more in videos. We use Adaptive Gamma Correction (AGC) to enhance the contrast and thus, get more details from the facial image. We use AGC as a pre-processing step in gender classification in still images. In videos, we propose a pipeline that quantifies the blurriness of an image using a blurriness metric (EMBM), and feeds it to its corresponding GenderCNN that was trained on faces with similar blurriness. We evaluated our proposed methods on challenging, large, and publicly available datasets, CelebA, IMDB-WIKI still images datasets and on McGill, and Point and Shoot Challenging (PaSC) videos datasets. Experiments show that we outperform or in some cases match the state of the art methods.

Paper Citation

in Harvard Style

Selim M., Sundararajan S., Pagani A. and Stricker D. (2018). Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 351-358. DOI: 10.5220/0006626103510358

in Bibtex Style

@conference{visapp18,
author={Mohamed Selim and Suraj Sundararajan and Alain Pagani and Didier Stricker},
title={Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={351-358},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006626103510358},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - Image Quality-aware Deep Networks Ensemble for Efficient Gender Recognition in the Wild
SN - 978-989-758-290-5
AU - Selim M.
AU - Sundararajan S.
AU - Pagani A.
AU - Stricker D.
PY - 2018
SP - 351
EP - 358
DO - 10.5220/0006626103510358
PB - SciTePress