Open AccessFreehand Sketching Portrait Recognition with Least Square CycleGAN
Author(s) -
Xiaosa Gou,
Bingguo Liu,
Guodong Liu,
Binghui Lu,
Yu Gan,
Fengdong Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1880/1/012016
Subject(s) - portrait , sketch , computer science , face (sociological concept) , matching (statistics) , artificial intelligence , computer vision , pattern recognition (psychology) , computer graphics (images) , speech recognition , art , visual arts , mathematics , algorithm , linguistics , statistics , philosophy
Freehand sketching portrait recognition refers to the recognition of sketched portraits and face photos drawn by artists. Existing research mainly involves inputting a given real portrait and converting it into a similar sketch portrait, and then matching the face with the sketch portrait in the real portrait database. This paper starts from the idea of inputting a sketch image to identify the real portrait in the database, focuses on the research based on the method of portrait synthesis, and introduces the existing methods of sketch portrait identification. We use the CycleGAN improved by least squares to achieve the translation from sketch to portrait, and finally PCA was used to complete face matching. The results show that LS_CycleGAN has certain advantages. Compared with other methods, its synthesis results are most close to the portrait. The average score of SSIM and PSNR is 0.844 and 17.331, and the average success rate of recognition is 88.2% for Rank10.