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

Abstract

Facial beauty prediction (FBP) is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this problem, various data-driven models, especially state-of-the-art deep learning techniques, were introduced, and benchmark dataset become one of the essential elements to achieve FBP. Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms. However, most of FBP benchmark datasets were built under specific computation constrains, which limits the performance and flexibility of the computational model trained on the dataset. In this paper, we argue that FBP is a multi-paradigm computation problem, and propose a new diverse benchmark dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) and diverse labels (face landmarks, beauty scores within [1,5], beauty score distribution), which allows different computational models with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression model for male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP using different combinations of feature and predictor, and various deep learning methods. The results indicates the improvement of FBP and the potential applications based on the SCUT-FBP5500.

Facial Beauty Prediction via Local Feature Fusion and Broad Learning System

“…2DPCA is capable of projecting the original features to the dimension with as much information as possible through reducing the dimension of the original features. In order to further verify the effectiveness of our method, we test on a smaller dataset SCUT-FBP5500 [44] for experiments. This is a facial beauty dataset with a beauty score ranging from 1 to 5, containing 5500 face images, which involves different races, genders and ages.…”

Beholder-Gan: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level

“…The trained model is then used to rate more images, thus creating a richer dataset. Since we condition the GAN on the distribution of scores and not on a single score, we train one predictive model per human rater, e.g., for the SCUT-FBP5500 dataset [7] with 60 distinct human raters, 60 models were trained to predict the scores assigned by each of them.…”

“…As explained in Section 2.3, we enrich our dataset by training a beauty predictor on one dataset and use it for labeling additional faces. To verify the validity of this idea, we trained a predictive model on the SCUT-FBP5500 dataset [7] and tested it on 200 random images from CelebAHQ [13]. 60 VGG models were trained, one per human rater, with the weights initialized from VGG trained on ImageNet.…”

“…Over the years, several datasets and methods for facial beauty prediction (FBP) have been suggested, e.g., [5,6]. Recently, a new dataset was published [7] that ranks facial image beauty by group of humans; unlike the previous datasets, the full score distribution for each subject was reported. Our work focuses on the task of generating facial images conditioned on their beauty score.…”

2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

“…images. SCUT-FBP5500 [6] is a facial beauty database of 5500 images, constructed by South China University of Technology. Our group has built a Large Scale Facial Beauty Database (LSFBD) [7], including 20,000 labeled images (10000 male images and 10000 female images) and 80000 unlabeled images.…”