A Multi-graph Fusion Based Manifold Embedding for Face Beauty Prediction

Kunwei Wang, Xiaoyi Feng, Fadi Dornaika, Dong Huang, Zhaoqiang Xia

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

Automatic facial beauty prediction is an interesting research topic in computer vision and aesthetic medicine. Most of the existing FBP methods rely on supervised solutions based on geometric features or deep features. Recently, multi-graph fusion techniques have been used to construct more accurate graphs which better represent the data. In this work, we propose a semi-supervised manifold embedding method in which multiple graphs with geometric features, deep features and label information are constructed. The proposed method fuses the geometric features with deep features to generate a high-level representation of a face image. Moreover, our method incorporated the label space information as a new form of graph, namely the Correlation Graph, with other similarity graphs. Furthermore, it updated the correlation graph to find a better representation of the data manifold. The experimental results on the SCUTFBP-5500 face beauty dataset demonstrated the superiority of the proposed algorithm compared with other state-of-the-art methods.

源语言英语
主期刊名Proceedings - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
出版商Institute of Electrical and Electronics Engineers Inc.
129-134
页数6
ISBN(电子版)9781665468725
DOI
出版状态已出版 - 2022
活动2022 International Conference on Image Processing and Media Computing, ICIPMC 2022 - Xi�an, 中国
期限: 27 5月 202229 5月 2022

出版系列

姓名Proceedings - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022

会议

会议2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
国家/地区中国
Xi�an
时期27/05/2229/05/22

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