New Challenges of Face Detection in Paintings based on Deep Learning

Siwar Bengamra, Olfa Mzoughi, André Bigand, Ezzeddine Zagrouba

2021

Abstract

In this work, we address the problem of face detection from painting images in Tenebrism style, a particular painting style that is characterized by the use of extreme contrast between light and dark. We use Convolutional Neural Networks (CNNs) to tackle this task. In this article, we show that face detection in paintings presents additional challenges as compared to classic face detection from natural images. For this, we present a performance analysis of three CNN architectures, namely, VGG16, ResNet50 and ResNet101, as backbone networks of one of the most popular CNN based object detector, Faster RCNN, to boost-up the face detection performance. This paper describes a collection and annotation of benchmark dataset of Tenebrism paintings. In order to reduce the impact of dataset bias, we propose to evaluate the effect of several data augmentation techniques used to increase variability. Experimental results reveal a detection average precision of 44.19% with ResNet101, while better performances have been achieved 79.48% and 83.94% with VGG16 and ResNet50, respectively.

Paper Citation

in Harvard Style

Bengamra S., Mzoughi O., Bigand A. and Zagrouba E. (2021). New Challenges of Face Detection in Paintings based on Deep Learning. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 311-320. DOI: 10.5220/0010243703110320

in Bibtex Style

@conference{visapp21,
author={Siwar Bengamra and Olfa Mzoughi and André Bigand and Ezzeddine Zagrouba},
title={New Challenges of Face Detection in Paintings based on Deep Learning},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={311-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010243703110320},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - New Challenges of Face Detection in Paintings based on Deep Learning
SN - 978-989-758-488-6
AU - Bengamra S.
AU - Mzoughi O.
AU - Bigand A.
AU - Zagrouba E.
PY - 2021
SP - 311
EP - 320
DO - 10.5220/0010243703110320
PB - SciTePress