  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. Thus, the results of our experiments indicate that the SCUT-FBP dataset
provides a reliable benchmark for facial beauty perception.
