CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation based on CelebA
원문 초록
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.
함께 살펴볼 논문
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MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution
Precise Facial Landmark Detection by Dynamic Semantic Aggregation Transformer
POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search
FaceXFormer: A Unified Transformer for Facial Analysis
STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration
토론
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