2015/11/08 by Duorui Xie, Lingyu Liang, Xie, Duorui +7 · 3 citations
Computer Science · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Evolutionary Psychology and Human Behavior #FOS: Computer and information sciences #Face recognition and analysis
paper · pdf · doi:10.48550/arxiv.1511.02459
openalex publication_date 2015/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.