文献类型：会议论文

英文题名：Deep Facial Features for Personalized Attractiveness Prediction

作者：Lebedeva, Irina[1];Guo, Yi[1];Ying, Fangli[1]

机构：[1]East China Univ Sci & Technol, 130 Meilong Rd, Shanghai, Peoples R China

会议论文集：13th International Conference on Digital Image Processing (ICDIP)

会议日期：MAY 20-23, 2021

会议地点：Singapore, SINGAPORE

语种：英文

外文关键词：Deep Learning; Facial Beauty Prediction; Feature Extraction

摘要：In this work, we propose a novel personalized facial attractiveness prediction method that is able to effectively learn an individual's preferences on few training images. A deep convolutional neural network (CNN) was first employed to estimate facial attributes. Then the attributes that play the most significant role for the individual were selected to train Random Forest. A new dataset specially created for personalized beauty evaluation was also proposed. Our method has achieved promising results of 54% Pearson's Correlation on 15 training images, 61% on 35 images.