YOLOv3: Face Detection in Complex Environments

Authors

Lin Zheng Chun^{1, 2}, Li Dian^{1, *,} , Jiang Yun Zhi^{1, *}, Wang Jing^{1, *}, Chao Zhang¹

¹School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China

²School of Computer Science, Guangdong University of Technology, Guangzhou, Guangdong 510665, China

^*Corresponding authors. Email: jiangyunzhi2008@163.com, 862729693@qq.com, wj_adr@163.com

DOI

10.2991/ijcis.d.200805.002How to use a DOI?

Keywords

Face detection; Complex environment; Priori box; Multiple score values

Abstract

Face detection has been well studied for many years. However, the problem of face detection in complex environments is still being studied. In complex environments, faces is often blocked and blurred. This article proposes applying YOLOv3 to face detection problems in complex environments. First, we will re-cluster the data set in order to find the most suitable a priori box. Then we set multiple score values to make it possible to predict the results of multiple sets of images and find the optimal score value. Experimental results show that after adjustment, the model has more advantages in face detection than the original model in complex environments. The average accuracy is more than 10% higher than that of aggregate channel feature (ACF), Tow-stage convolutional neural network (CNN) and multi-scale Cascade CNN in face detection benchmarks WIDER FACE. Our code is available in: git@github.com:Mrtake/-complex--scenes-faceYOLOv3.git

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Lin Zheng Chun
AU  - Li Dian
AU  - Jiang Yun Zhi
AU  - Wang Jing
AU  - Chao Zhang
PY  - 2020
DA  - 2020/08/17
TI  - YOLOv3: Face Detection in Complex Environments
JO  - International Journal of Computational Intelligence Systems
SP  - 1153
EP  - 1160
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200805.002
DO  - 10.2991/ijcis.d.200805.002
ID  - Chun2020
ER  -