Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74339
Title: Comparison and fusion of multiple types of, features for image-based facial beauty, prediction
Authors: Chen, F
Zhang, D 
Wang, C
Duan, X
Keywords: Facial beauty
Feature extraction
Fusion
Issue Date: 2017
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10568, p. 23-30 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Facial beauty prediction is an emerging research topic that has many potential applications. Existing works adopt features either suggested by putative rules or borrowed from other face analysis tasks, without an optimization procedure. In this paper, we make a comprehensive comparison of different types of features in terms of facial beauty prediction accuracy, including the rule-based features, global features, and local descriptors. Each type of feature is optimized by dimensionality reduction and feature selection. Then, we investigate the optimal fusion strategy of multiple types of features. The results show that the fusion of AAM, LBP, and PCANet features obtains the best performance, which can serve as a competitive baseline for further studies.
Description: 12th Chinese Conference on Biometric Recognition, CCBR 2017, 28 - 29 October 2017
URI: http://hdl.handle.net/10397/74339
ISBN: 9783319699226
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-69923-3_3
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