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Face shadow detection was usually used as a preprocessing step for face image tasks to avoid interference caused by shadows. Aiming at this problem, a face shadow detection scheme based on deep learning was proposed. Firstly, the shadow areas of the 1 600 unevenly illuminated face images in the Yale face database according to the illumination and face structure information. Second, a face shadow detection network based on the nested U-Net structure and attention module was designed. The nested U-Net structure was used to extract high-resolution information and global features of the images. The attention module was used to fuse the output of each nested U-Net and suppress the noise information brought by low-order sub-layers. The network was evaluated on the labeled face detection data set. The experimental results showed that the average detection error rate of the proposed method was reduced by 14.2% compared with the optimal solution in the control group, which could effectively detect small shadows in the image, and provide more accurate shadow edge positioning.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2021472
China Classification Code:TP391.41
Citation Information:
[1]LIU Jiaqi,YI Jizheng,CHEN Aibin.FSDN:a Nested U-Net for Face Shadow Detection[J].Journal of Zhengzhou University(Natural Science Edition),2023,55(02):51-56.DOI:10.13705/j.issn.1671-6841.2021472.
Fund Information:
国家自然科学基金项目(61602528); 湖南省研究生科研创新基金项目(CX20200740); 中南林业科技大学先进人才项目(2015YJ013)
2022-05-19
2022-05-19
2022-05-19