Low-resolution Face Detection Algorithm Based on Super-resolution Reconstruction
WANG Guohui
CHEN Jianmei
Abstract：Low-resolution face detection has important applications in video surveillance and other fields.However,current face detection algorithms are not ideal in low-resolution face detection.For this problem,the paper proposes a low-resolution face detection algorithm based on super-resolution reconstruction.First,most of the normal faces can be detected by the prepositioned basic face detector.Secondly,by lowering the category confidence threshold.the regions proposal that may contain faces are sent to the super-resolution reconstruction network(MGAN)based on the improved GAN to further complete the face detection task.Final-ly,the face regions are summarized and the non-maximum suppression algorithm is used to obtain the final detection results.The ex-perimental results show that in the WIDERFACE data set,compared with the mainstream face detection algorithms such as S3FD,the proposed algorithms have higher detection accuracy,and the improvement is obvious in the hard subset.
Keywords：super-resolutiongenerative adversarial netface detectionlow-resolution
Publication Date：2024-02-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 315-320 )