<- Back to Datasets

SCUT-FBP5500 dataset

A diverse benchmark database for multi-paradigm facial beauty prediction

SCUT-FBP5500 dataset

Facial beauty prediction is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. And the benchmark dataset is one of the most essential elements to achieve computation-based facial beauty prediction. Current datasets pertaining to facial beauty prediction are small and usually restricted to a very small and meticulously prepared subset of the population (e.g. ethnicity, gender and age). To tackle this problem, we build a new diverse benchmark dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty prediction.The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (facial landmarks, beauty scores in 5 scales, beauty score distribution), which allows different computational model with different facial beauty prediction paradigms, such as appearance-based/shape-based facial beauty classification/regression/ranking model for male/female of Asian/Caucasian.

View this Dataset
->
Task
Face Recognition
Annotation Types
Bounding Boxes
Items
Classes
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Unknown
Label your own datasets on V7
Try our trial or talk to one of our experts.