Computer Science ›› 2022, Vol. 49 ›› Issue (11): 141-147.doi: 10.11896/jsjkx.220600012

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Edge Guided Self-correction Skin Detection

ZHENG Shun-yuan, HU Liang-xiao, LYU Xiao-qian, SUN Xin, ZHANG Sheng-ping   

  1. College of Computer Science and Technology,Harbin Institute of Technology,Weihai,Shandong 264209,China
  • Received:2022-06-02 Revised:2022-07-22 Online:2022-11-15 Published:2022-11-03
  • About author:ZHENG Shun-yuan,born in 1997,postgraduate.His main research interests include computer vision and deep lear-ning.
    LYU Xiao-qian,born in 1995,postgra-duate.Her main research interests include object detection,image enhancement and video analysis.
  • Supported by:
    National Natural Science Foundation of China(61872112) and Taishan Scholars Program of Shandong Province(tsqn201812106).

Abstract: Skin detection has been a widely studied computer vision topic for many years,whereas remains a challenging task.Previous methods celebrate their success in various ordinary scenarios but still suffer from fragmentary prediction and poor generalization.To address this issue,this paper proposes an edge guided network driven by a massive self-corrected skin detection dataset for robust skin detection.To be specific,a multi-task learning based network which conducts skin detection and edge detection jointly is proposed.The predicted edge map is further converged to the skin detection stream via an edge attention module.Meanwhile,to engage a large-scale of low-quality data from the human parsing task to strengthen the generalization of the network,a self-correction algorithm is adapted to prune the side effect of supervised by noisy labels with continuously polishing up those defects during the training process.Experimental results indicate that the proposed method outperforms the state-of-the-art in skin detection.

Key words: Skin detection, Edge detection, Multi-task learning, Self-correction algorithm

CLC Number: 

  • TP391
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