Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (2): 191-201.doi: 10.11896/jsjkx.231100166

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Face Detection Algorithm Based on Multi-task Joint Learning in Weak Light Scenes

ZHANG Xia1, SU Zhaohui1,2, CHEN Lu1,2   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 Institute of Big Data Science and Industry,Shanxi University,Taiyuan 030006,China
  • Received:2023-11-27 Revised:2024-04-29 Online:2025-02-15 Published:2025-02-17
  • About author:ZHANG Xia,born in 1977,Ph.D,asso-ciate professor,is a member of CCF(No.22705M).Her main research interests include artificial intelligence,machine learning and image processing.
    CHEN Lu,born in 1991,Ph.D,asso-ciate professor,is a member of CCF(No.E0588M).His main research interests include robotic grasping and image enhancement.
  • Supported by:
    National Natural Science Foundation of China(62072291,62373233,62003200),Science and Technology Major Project of the Ministry of Science and Technology of Shanxi Province,China(202201020101006) and Central Government Guiding Funds for Local Science and Technology Development(YDZJSX20231B001).

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Abstract: In weak light scenes,face detection refers to the use of image processing techniques to detect faces.Currently,most face detection algorithms in weak light environments typically enhance the weak light images before performing face detection,neglecting the feature correlation between face detection and image enhancement,thereby affecting the generalization ability of the model.Inspired by the EnlightenGAN algorithm,this paper proposes a Multitask joint learning algorithm for face detection in weak light environments.First,it integrates the input layer shared representation of face detection and image enhancement.Se-cond,it combines the face attention network with EnlightenGAN,adding a local discriminator for face region determination based on the global-local discriminator.Finally,it introduces illumination weight parameters on the basis of self-regularized attention maps,adjusting them to optimize the accuracy of face detection.Experimental results on the DARK FACE dataset demonstrate that,compared with existing algorithms,the proposed algorithm achieves a 1.92% improvement in face detection accuracy,while also effectively enhances the visual quality of images captured under weak light conditions.

Key words: Weak light environment, Face detection, Image enhancement, Multi-task joint learning, Local discriminator

CLC Number: 

  • TP391
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