A diverse benchmark database for multi-paradigm facial beauty prediction
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.