CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation Based on CelebA

Research question and approach

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.

When this work is relevant

Cite this dataset when using its hairstyle-recommendation annotations or task formulation. Consult the original work and dataset terms before reuse.

Author-written abstract

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.

Abstract source: https://arxiv.org/abs/2104.06885. Checked 2026-09-14. Bibliographic metadata uses the linked paper record or author-maintained catalog. Results, limitations and experimental settings remain defined by the original source.

Citation

BibTeX · CITATION.cff

BibTeX is preserved from the author-maintained citation repository. The CFF uses the source metadata shown on this page. Version titles or author lists can differ; choose the version you used.

@inproceedings{chen2021celebhair,
  title={Celebhair: A new large-scale dataset for hairstyle recommendation based on celeba},
  author={Chen, Yutao and Zhang, Yuxuan and Huang, Zhongrui and Luo, Zhenyao and Chen, Jinpeng},
  booktitle={International Conference on Knowledge Science, Engineering and Management},
  pages={323--336},
  year={2021},
  organization={Springer}
}