Abstract
In efforts to build a hairstyle recommendation application, we are confronted with the lack of relevant hairstyle-related datasets. 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.
Y. Chen and Y. Zhang—Co-first authors with equal contribution.
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Acknowledgments
This work is supported by Research Innovation Fund for College Students of Beijing University of Posts and Telecommunications, and National Natural Science Foundation of China under Grant No. 61702043.
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Chen, Y., Zhang, Y., Huang, Z., Luo, Z., Chen, J. (2021). CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation Based on CelebA. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_27
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