# SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception Source: [SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:13:30.351Z Authors: Duorui Xie, Lingyu Liang, Lianwen Jin, Jie Xu, Mengru Li Published: 2015-11-08T09:21:32.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Facial attractiveness estimation on SCUT-FBP 20% (test)](): Facial attractiveness estimation; SCUT-FBP 20% (test); MAE: 0.393. - [Facial Beauty Prediction on SCUT-FBP](): Facial Beauty Prediction; SCUT-FBP; PCC: 0.8187. ## Original sources and implementation [Original paper]() [PDF]()