CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation based on CelebA
This provides a new dataset for hairstyle recommendation, which is incremental as it builds on existing data with added features.
The authors introduced CelebHair, a large-scale dataset for hairstyle recommendation built from CelebA, with added facial features and classifications. They demonstrated its superiority over existing datasets in variety, veracity, and volume through empirical comparison.
In this paper, we present a new large-scale dataset for hairstyle recommendation, CelebHair, based on the celebrity facial attributes dataset, CelebA. Our dataset inherited the majority of facial images along with some beauty-related facial attributes from CelebA. Additionally, we employed facial landmark detection techniques to extract extra features such as nose length and pupillary distance, and deep convolutional neural networks for face shape and hairstyle classification. Empirical comparison has demonstrated the superiority of our dataset to other existing hairstyle-related datasets regarding variety, veracity, and volume. Analysis and experiments have been conducted on the dataset in order to evaluate its robustness and usability.