文献类型：期刊文献

英文题名：MEBeauty: a multi-ethnic facial beauty dataset in-the-wild

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

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2022

卷号：34

期号：17

起止页码：14169

外文期刊名：NEURAL COMPUTING & APPLICATIONS

收录：;EI(收录号：20214010988182);WOS:【SCI-EXPANDED(收录号:WOS:000703374300001)】；

语种：英文

外文关键词：Facial beauty prediction; Deep learning; Robust loss functions; Knowledge transfer; Regression

摘要：Although facial beauty prediction (FBP) has achieved high accuracy on images captured in a constrained environment, it is still a challenging task on face images in-the-wild. Moreover, there is no FBP benchmark dataset that includes images with various ethnic, age, gender properties and unrestricted in terms of face expression and pose. In this work, the issue of FBP in real-life scenario is addressed and a multi-ethnic facial beauty dataset, namely MEBeauty, is introduced. All face images are captured in an unconstrained environment and rated by volunteers with various ethnicity, age and gender in order to avoid any cultural and social biases in beauty perception. Different well-known CNNs with layer-wise transfer learning are performed on the dataset. Moreover, the evaluation of knowledge learning from the face recognition task across FBP is conducted. The expected high number of aberrant and outlier faces is considered and the use of various robust loss functions in order to learn deep regression networks for facial beauty prediction is evaluated. Several FBP frameworks are performed on the proposed dataset and widely-used SCUT-FBP 5500 in order to compare their effectiveness on face images in constrained and unconstrained environments.