Facial attractiveness computation by label distribution learning with deep CNN and geometric features

Shu Liu, Bo Li, Yang Yu Fan, Zhe Quo, Ashok Samal

科研成果: 书/报告/会议事项章节会议稿件同行评审

17 引用 (Scopus)

摘要

Facial attractiveness computation is a challenging task because of the lack of labeled data and discriminative features. In this paper, an end-to-end label distribution learning (LDL) framework with deep convolutional neural network (CNN) and geometric features is proposed to meet these two challenges. Different from the previous work, we recast this task as an LDL problem. Compared with the single label regression, the LDL could improve the generalization ability of our model significantly. In addition, we propose some kinds of geometric features as well as an incremental feature selection method, which could select hundred-dimensional discriminative geometric features from an exhaustive pool of raw features. More importantly, we find these selected geometric features are complementary to CNN features. Extensive experiments are carried out on the SCUT-FBP dataset, where our approach achieves superior performance in comparison to the state-of-the-arts.

源语言英语
主期刊名2017 IEEE International Conference on Multimedia and Expo, ICME 2017
出版商IEEE Computer Society
1344-1349
页数6
ISBN(电子版)9781509060672
DOI
出版状态已出版 - 28 8月 2017
活动2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, 香港
期限: 10 7月 201714 7月 2017

出版系列

姓名Proceedings - IEEE International Conference on Multimedia and Expo
0
ISSN(印刷版)1945-7871
ISSN(电子版)1945-788X

会议

会议2017 IEEE International Conference on Multimedia and Expo, ICME 2017
国家/地区香港
Hong Kong
时期10/07/1714/07/17

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