  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.
