computer-vision

Facial Beauty Prediction

3260 papers • 126 benchmarks • 313 datasets

Facial beauty prediction is the task of predicting the attractiveness of a face. ( Image credit: SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in facial-beauty-prediction

Trend

Dataset

Best Model

Actions

SCUT-FBP

MEBeauty

ECCV HotOrNot

Libraries

i

Use these libraries to find facial-beauty-prediction models and implementations

Datasets

No datasets available.

Subtasks

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Most implemented papers

SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

Lianwen Jin, Lingyu Liang, Luojun Lin, Duorui Xie, Mengru Li•Thu Jan 18 2018

It is argued that FBP is a multi-paradigm computation problem, and a new diverse benchmark dataset, called SCUT-FBP5500, is proposed to achieve multi-Paradigm facial beauty prediction.

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Transferring Rich Deep Features for Facial Beauty Prediction

Lu Xu, Jinhai Xiang, Xiaohui Yuan•Mon Mar 19 2018

A method is proposed which transfers rich deep features from a pretrained model on face verification task and feeds the features into Bayesian ridge regression algorithm for facial beauty prediction and achieves improved or comparable performance on SCUT-FBP dataset and ECCV HotOrNot dataset.

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MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction

Luojun Lin, Zhifeng Shen, Jia-Li Yin, Qipeng Liu, Yuanlong Yu, Weijie Chen•Wed Oct 25 2023

A novel MetaFBP framework is proposed, in which a universal feature extractor is devised to capture the aesthetic commonality and then optimized to adapt the aesthetic individuality by shifting the decision boundary of the predictor via a meta-learning mechanism.

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MEBeauty: a multi-ethnic facial beauty dataset in-the-wild

I. Lebedeva, Yi Guo, Fangli Ying•Sun Oct 03 2021

The issue of FBP in real-life scenario is addressed and a multi-ethnic facial beauty dataset, namely MEBeauty, is introduced and the evaluation of knowledge learning from the face recognition task across FBP is conducted.

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Facial Beauty Analysis Using Distribution Prediction and CNN Ensembles

Ahmed Aman Ibrahim, N. Ugail, Tazkia Hoodh Jayatileke, Millie Hope Saffery, Hassan Ugail•Thu Dec 07 2023

Facial Beauty Prediction (FBP) is a computer vision task of quantifying the beauty of a face. Several solutions to this problem have benefitted immensely from the recent developments in deep learning. However, the majority of current methods train machine learning models to purely predict mean beauty scores, treating FBP solely as a regression task. In addition, deep learning based FBP approaches so far use transfer learning from models trained on general classification tasks such as ImageNet. We propose fine-tuning an ensemble of convolutional neural network (CNN) models originally trained on face verification tasks using a variety of loss functions such as Earth Mover's Distance (EMD) based loss. With this approach, our method can predict the entire beauty score distribution rather than just the mean, and the predicted mean scores have a higher Pearson Correlation (PC) compared to the ground truth scores. This method achieves state of the art results on the MEBeauty dataset in terms of mean absolute error, root mean squared error and PC between the predicted and the ground truth mean scores.

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