SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception

In this paper, a novel face dataset with attractiveness ratings, namely the SCUT-FBP dataset, is developed for automatic facial beauty perception. This dataset provides a benchmark to evaluate the performance of different methods for facial attractiveness prediction, including the state-of-the-art d...

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Bibliographic Details
Published in2015 IEEE International Conference on Systems, Man, and Cybernetics pp. 1821 - 1826
Main Authors Duorui Xie, Lingyu Liang, Lianwen Jin, Jie Xu, Mengru Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2015
Subjects
Benchmark testing
Correlation
Face
Face dataset
facial attractiveness prediction
facial beautification
facial beauty assessment
Feature extraction
Machine learning
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Summary:In this paper, a novel face dataset with attractiveness ratings, namely the SCUT-FBP dataset, is developed for automatic facial beauty perception. This dataset provides a benchmark to evaluate the performance of different methods for facial attractiveness prediction, including the state-of-the-art deep learning method. The SCUT-FBP dataset contains face portraits of 500 Asian female subjects with attractiveness ratings, all of which have been verified in terms of rating distribution, standard deviation, consistency, and self-consistency. Benchmark evaluations for facial attractiveness prediction were performed with different combinations of facial geometrical features and texture features using classical statistical learning methods and the deep learning method. The best Pearson correlation 0.8187 was achieved by the CNN model. The results of the experiments indicate that the SCUT-FBP dataset provides a reliable benchmark for facial beauty perception.
DOI:10.1109/SMC.2015.319