SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception
This provides a reliable benchmark for researchers in computer vision and psychology working on facial attractiveness prediction, though it is incremental as it focuses on a specific demographic.
The authors introduced the SCUT-FBP dataset, a benchmark with attractiveness ratings for 500 Asian female faces, to tackle automatic facial beauty perception, achieving a best Pearson correlation of 0.8187 using a CNN model.
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.