文献类型：期刊文献

英文题名：Personalized facial beauty assessment: a meta-learning approach

作者：Lebedeva, Irina[1];Ying, Fangli[2,5];Gu, Yi[2,3,4]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[3]Business Intelligence & Visualizat Res Ctr, Natl Engn Lab Big Data Distribut & Exchange Techn, Shanghai 200436, Peoples R China;[4]Shanghai Engn Res Ctr Big Data & Internet Audienc, Shanghai 200072, Peoples R China;[5]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：39

期号：3

起止页码：1095

外文期刊名：VISUAL COMPUTER

收录：;EI(收录号：20220811671905);WOS:【SCI-EXPANDED(收录号:WOS:000754122600001)】；

基金：This research is financially supported by The National Key Research and Development Program of China (grant number 2018YFC0807105) and Science and Technology Committee of Shanghai Municipality (STCSM) (under grant numbers 17DZ1101003, 18511106602 and 18DZ2252300). Partially Supported by Open Funding Project of the State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China; Also Supported by National Key Research and Development Program of China (No. 2020YFA0907800).

语种：英文

外文关键词：Meta-learning; Facial beauty prediction; Deep learning

摘要：Automatic facial beauty assessment has recently attracted a growing interest and achieved impressive results. However, despite the obvious subjectivity of beauty perception, most studies are addressed to predict generic or universal beauty and only few works investigate an individual's preferences in facial attractiveness. Unlike universal beauty assessment, an effective personalized method is required to produce a reasonable accuracy on a small amount of training images as the number of annotated samples from an individual is limited in real-world applications. In this work, a novel personalized facial beauty assessment approach based on meta-learning is introduced. First of all, beauty preferences shared by an extensive number of individuals are learnt during meta-training. Then, the model is adapted to a new individual with a few rated image samples in the meta-testing phase. The experiments are conducted on a facial beauty dataset that includes faces of various ethnic, gender, age groups and rated by hundreds of volunteers with different social and cultural backgrounds. The results demonstrate that the proposed method is capable of effectively learning personal beauty preferences from a limited number of annotated images and outperforms the facial beauty prediction state-of-the-art on quantitative comparisons.