Abstract：In view of the fact that facial beautification has been widely studied,but the lack of effective beautification image quality evaluation methods limits the further development of beautification technology,a no-reference evaluation method for facial beautification image quality was proposed. In this method,the facial cognition and perception were combined with the facial beautification technologies to unearth the quality representation of beautified images. Firstly,a facial beautification image database was constructed,the facial image was decomposed to three areas:skin,eyes and mouth. Then,facial aesthetic features were extracted from five aspects:skin color,smoothness,illumination,grayscale difference and sharpness. Finally,Support Vector Regression(SVR)was used to train the facial beautification quality model and predict the quality of the beautified image. The experimental results show that the proposed method achieves 0. 920 5 and 0. 900 9 respectively in the Pearson linear correlation coefficient and Spearman RankOrder Correlation Coefficient(SROCC) on the proposed database,which are higher than those of image quality evaluation methods BIQI(Blind Image Quality Indices),and NIQE(Natural Image Quality Evaluation).
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2020, Vol. 40 
