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

I Introduction

Assessing facial beauty seems natural for human being, but an absolute definition of facial beauty remains elusive. Recently, facial beauty prediction (FBP) have attracted ever-growing interest in the pattern recognition and machining learning communities

[8, 10, 9], which aims to achieve automatic human-consistent facial attractiveness assessment by a computational model. It has application potential in facial makeup synthesis/recommendation [10, 11, 12]

, content-based image retrieval

[16], aesthetic surgery [10], or face beautification [4, 6, 5, 7].

Fig. 1: The images with different facial properties and beauty scores from the proposed SCUT-FBP5500 benchmark dataset. The dataset download URL is shown below the title.

Database	Image Num.	Labelers/Image	Beauty Class	Face Property	Face Landmarks	Publicly Available
Y. Eisenthal et al. [15]	184	28 or 18	7	Caucasian Female	$\times$	$\times$
F. Chen et al. [22]	23412	unknown	2	Asian Male/Female	$\sqrt$	$\times$
H. Gunes et al. [35]	215	46	10	Female	$\sqrt$	$\times$
J. Fan et al. [23]	432	30	7	Generated Female	$\sqrt$	$\times$
M. Redi et al. [34]	10141	78-549	10	Multiple (Sampled from AVA [16])	$\times$	$\sqrt$
SCUT-FBP [1]	500	70	5	Asian Female	$\sqrt$	$\sqrt$
SCUT-FBP5500	5500	60	5	Asian/Caucasian; Male/Female	$\sqrt$	$\sqrt$

TABLE I: Representative Databases for Facial Beauty Prediction

From the computational perspective, FBP is still a challenging problem. It is involved with the formulation of visual representation and predictor for the abstract concept of facial beauty. To tackle this problem, various data-driven models, were introduced into FBP. One line of the works follows the classic pattern recognition process, which constructs the FBP system using the combination of the hand-crafted features and the shallow predictors. The related hand-crafted feature derived from visual recognition includes the geometric features, like the geometric ratios and landmark distances [8, 20, 15, 19, 23, 21], and the texture features, like the Gabor-/SIFT-like features [25, 26, 24]. Then, a shallow FBP predictoris trained by the extracted feature in a statistical manner.

Another line of works is advanced by the reviving of neural networks, especially the stat-of-the-art deep learning

[14]. The hierarchial structure of the deep learning model allows to build an end-to-end FBP system that automatically learns both the representation and the predictor of facial beauty simultaneously from the data. Many works indicate that FBP based on deep learning is superior to the shallow predictors with hand-crafted facial feature [30, 29, 33, 3, 2, 1].

Most of current FBP models are data-driven, which makes benchmark dataset become one of the essential elements for FBP. There have been many works of the benchmark datasets [23, 24, 35, 30, 33, 22, 16, 34] involved with FBP, but most of these datasets focus on a specific problem with specific computation constrains, as shown in Table I. Yan et al. [24] regarded FBP as a ranking problem, and proposed a dataset with low-resolution images gathered from social networks. Fan et al. [23] focused on the geometry analysis of FBP and proposed a dataset containing computer-generated faces with different facial proportions. The Northeast China database [22], Shanghai database [21], Hot-Or-Not database [32, 33], AVA database [16] and re-sampled face subset of AVA database [34] are large-scale databases involved with FBP, where the Northeast China [22] and Shanghai database [21] are limited for geometric facial beauty analysis without attractiveness ratings; Hot-Or-Not database [32, 33] only focuses on the appearance-based FBP; and the AVA database [16, 34] is originally designed for aesthetic analysis of entire images but not the facial attractiveness. In our previous work, Xie et al. [1] published a SCUT-FBP benchmark dataset, which has led to many FBP models [1, 3, 2, 31, 37], especially the hierarchial CNN-based FBP models with the state-of-the-art deep learning [1, 3, 2, 37]. Despite the prevalent usage of the SCUT-FBP, it only contains 500 Asian Female faces, which may limit the performance of the data-demanded model for FBP.

We find that FBP have been formulated the recognition of facial beauty as a specific supervised learning problem of classification [27, 20, 15, 30], regression [19, 13, 23, 29, 38] or ranking [24, 25, 37]. It indicates that FBP is intrinsically a computation problem with multiple paradigms. Previous databases built under specific computation constrains would limit the performance and flexibility of the computational model trained on the dataset, and it is difficult to compare different models derived from the dataset with specific computation paradigm. Therefore, this paper argues that FBP is a multi-paradigm computation problem, and proposes 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 landmark, beauty score, beauty score distribution), which allows different computational model with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression/ranking model for male/female with Asian/Caucasian. Furthermore, the diverse faces with beauty scores gathered from 60 different labelers can lead to many interesting research, such as cross-culture facial beauty analysis, personalized FBP [36], or automatic face beautification [4, 6, 5, 7]. Both shallow prediction model with hand-crafted feature and the state-of-the-art deep learning models were evaluated on the dataset, and the results indicates the improvement of FBP and the potential applications by the SCUT-FBP5500.

The main contributions of this paper can be summarized as following:

Dataset. We propose a new large-scale SCUT-FBP5500 benchmark dataset that has totally 5500 frontal faces with diverse properties and diverse labels, which allows construction of FBP models with different paradigms.
Benchmark Analysis. We analyze the samples, score labels, labelers and facial landmarks of the SCUT-FBP5500 statistically, and the visualization of the data illustrates the properties of the SCUT-FBP5500.
Facial Beauty Prediction Evaluation. Both shallow prediction model with hand-crafted feature and deep learning models are trained on the SCUT-FBP5500 for evaluation, and the results indicates the improvement of FBP based on the proposed dataset with better diversity.

Ii Construction of SCUT-FBP5500 Dataset

Ii-a Face Images Collection

The SCUT-FBP5500 Dataset contains 5500 frontal, un-occluded faces aged from 15 to 60 with neutral expression. It can be divided into four subsets with different races and gender, including 2000 Asian females, 2000 Asian males, 750 Caucasian females and 750 Caucasian males. Most of the images of the SCUT-FBP5500 were collected from Internet, where some portions of Asian faces were from the DataTang [39] and some Caucasian faces were from the 10k US Adult database [40].

Ii-B Facial Beauty Scores and Facial Landmarks

Subset	CF	CM	AF	AM
Total Score Num.	45000	45000	120000	120000
Outlier Num.	143	181	356	497
Outlier Portion	$0.3 %$	$0.4 %$	$0.3 %$	$0.4 %$

TABLE II: The Outlier Number and Portion of Beauty Scores of Caucasian female (CF), Caucasian male (CM), Asian female (AF) and Asian male (AM).

All the images are labeled with beauty scores ranging from [1, 5] by totally 60 volunteers aged from 18-27 (average 21.6), where the beauty score 5 means most attractive and so on. We developed a web-based GUI system to obtain the facial beauty scores. The labeling system was deployed on the Ali Cloud, and the labeling tasks are distributed to each volunteer in crowdsourcing manners. The four subset, Asian male/female and Caucasian male/female, are labeled separately, where each face of the subset are randomly shown to the volunteer. Then, the volunteer are asked to select the beauty scores within [1, 5] for the face. To reduce the variance in the labeling process, about

10 %

faces recurred randomly. If the correlation coefficient of the two beauty score of the same faces is less than 0.7, the volunteer would be asked to rate this face once more to decide the final score.

To allow geometric analysis of facial beauty, 86 facial landmarks are located to the significant facial components of each images, such as eyes, eyebrows, nose and mouth. A GUI landmarks location system is developed, where the original location of the landmarks are initialized by the active shape model (ASM) trained by the SCUT-FBP dataset. Then, the detected landmarks by ASM are modified manually by volunteers to ensure the accuracy.

Fig. 3:
Distribution of standard deviations of Caucasian female, Asian female, Caucasian male and Asian male, respectively.

Iii Benchmark Analysis of the SCUT-FBP5500

We made benchmark analysis of the beauty scores, labelers and face landmarks of the SCUT-FBP5500 with different gender and races, including Asian female (AF), Asian male (AM), Caucasian female (CF) and Caucasian male (CM).

	CF	AF	CM	AM	All Faces
Female Labelers	0.785	0.800	0.747	0.793	0.785
Male Labelers	0.791	0.795	0.763	0.797	0.781
All Labelers	0.788	0.785	0.743	0.782	0.770

TABLE III: Correlation Coefficients between Male and Female Labelers for Beauty Score of Caucasian female (CF), Caucasian male (CM), Asian female (AF) and Asian male (AM).

Fig. 5: Correlation Coefficient of male and female labelers for beauty score of CF, AF, CM, AM and all the faces in SCUT-FBP5500.

Iii-a Distribution of Beauty Scores

We visualize the distribution of the beauty scores of AF, AM, CF and CM, respectively. To obtain better visualization, we preprocess the data and filter the outliers of the beauty scores. We regard the average score of all the 60 labelers as the ground-truth. If the score of specific labeler for the same face differs from the ground-truth over 2, the score is treated as outlier and is removed from the distribution visualization. The number and portion of the outliers in the four subset are listed in Table II, and the small portion of outlier indicate the reliability of the labeling process for beauty score.

Since the outlier portion of the beauty score is tiny, the distributions of the original data and the preprocessed data is mostly similar. Therefore, we visualized the score distribution of the SCUT-FBP5500 using the preprocessed data for all the four subset, respectively. Two distribution fitting schemes are used: one is Gaussian fitting (yellow curve), the other is piecewise fitting (red and blue curve), as shown in Fig. 2. The results indicates that the beauty scores of all the four subset can be approximately fitted by a mixed distribution model with two Gaussian components.

Iii-B Standard Deviation of Beauty Scores

We calculate the standard deviation of the scores gathered from different labelers to the ground-truth, and illustrate the results as histogram in Fig. 3 and as box figure in Fig. 4

. We observe that the distribution of standard deviations is similar to Gaussian distribution, and most of the standard deviations are within a reasonable range of [0.6, 0.7].

Iii-C Correlation of Male/Female Labelers

In this subsection, we investigate the correlation between the male and female Asian labelers for the beauty scores, as shown in Table III and Fig. 5. We observe that the correlation of Asian faces is persistently larger than Caucasian. It is consistent to the psychological research that human have better facial beauty perception for the faces from the same race.

Iii-D PCA Analysis of Facial Geometry

We visualize the 86-points face landmarks of the dataset using principle component analysis (PCA). Fig. 6 illustrate the mean and the five first principle component of the facial geometry of Asian female (AF) and Asian male (AM), where the landmarks data of Caucasian share similar distribution to Asian faces. We observe that the face shape is one of the main component influence the face geometry of beauty, which is consistent to related psychological research and previous works in [21, 22]

Iv FBP Evaluation via Hand-Crafted Feature and Shallow Predictor

This section, we evaluate the SCUT-FBP5500 using the hand-crafted feature with shallow predictor, while the next section introduce some state-of-the-art deep learning model to achieve FBP.

Iv-a Geometric Feature with Shallow Predictor

We extract a 18-dimensional ratio feature vector from the faces and formulate FBP based on different regression models, such as the linear regression (LR), Gaussian regression (GR), and support vector regression (SVR). Comparison were performed for Caucasian female/male and Asian female/male subsets, and the performance of different model are measured using pearson correlation coefficient (PC)

[15], maximum absolute error (MAE) and root mean square error (RMSE) after 10 folds cross validation. The results are listed in Table IV and Table V, which can be regarded as a baseline for the geometric analysis of FBP.

	Asian Female			Asian Male
	LR	GR	SVR	LR	GR	SVR
PC	0.6771	0.7057	0.7008	0.6348	0.6923	0.6816
MAE	0.402	0.387	0.3876	0.3894	0.3572	0.356
RMSE	0.5246	0.5057	0.5089	0.5085	0.4752	0.4823
	Caucasian Female			Caucasian Male
	LR	GR	SVR	LR	GR	SVR
PC	0.6809	0.7263	0.7093	0.6063	0.63	0.6397
MAE	0.3986	0.3862	0.4001	0.3871	0.3689	0.3617
RMSE	0.5239	0.4908	0.5087	0.4899	0.4784	0.4739

TABLE IV: Facial beauty prediction using geometric feature with shallow models for subsets of different races and gender

	Linear Regression	Gaussian Regression	SVR
PC	0.5948	0.6738	0.6668
MAE	0.4289	0.3914	0.3898
RMSE	0.5531	0.5085	0.5132

TABLE V: Facial beauty prediction using Geometric Feature with shallow models for the whole dataset

	86-keypoints		64UniSample
	GR	SVR	GR	SVR
PC	0.7472	0.6691	0.6764	0.8065
MAE	0.3554	0.3891	0.4014	0.3976
RMSE	0.4599	0.5065	0.5177	0.5126

TABLE VI: Facial beauty prediction using Gabor feature with two sampling scheme on whole dataset

PC	1	2	3	4	5	Average
AlexNet	0.8667	0.8645	0.8615	0.8678	0.8566	0.8634
ResNet-18	0.8847	0.8792	0.8929	0.8932	0.9004	0.89
ResNeXt-50	0.8985	0.8932	0.9016	0.899	0.9064	0.8997
MAE	1	2	3	4	5	Average
AlexNet	0.2633	0.2605	0.2681	0.2609	0.2728	0.2651
ResNet-18	0.248	0.2459	0.243	0.2383	0.2383	0.2419
ResNeXt-50	0.2306	0.2285	0.226	0.2349	0.2258	0.2291
RMSE	1	2	3	4	5	Average
AlexNet	0.3408	0.3449	0.3538	0.3438	0.3576	0.3481
ResNet-18	0.3258	0.3286	0.3184	0.3107	0.2994	0.3166
ResNeXt-50	0.3025	0.3084	0.3016	0.3044	0.2918	0.3017

TABLE VII: Comparison of AlexNet [41], ResNet-18 [42] and ResNeXt-50 [43] in measurement of PC, MAE and RMSE by 5-fold cross validation

	AlexNet	ResNet-18	ResNeXt-50
PC	0.8298	0.8513	0.8777
MAE	0.2938	0.2818	0.2518
RMSE	0.3819	0.3703	0.3325

TABLE VIII: Comparison of AlexNet [41], ResNet-18 [42] and ResNeXt-50 [43] in measurement of PC, MAE and RMSE by

60 %

training and

40 %

testing

Iv-B Appearance Feature with Shallow Predictor

Fig. 7: Two sampling schemes to extract the appearance feature of FBP, where the left one is the 86-keypoints method and the right one is the UniSamplePoint method.

We extract 40 Gabor feature maps from every original image in five directions and eight angles. Then, we obtain the appearance feature of FBP using two different sampling schemes that extracts some component of the Gabor feature maps as following:

Sample 86-keypoints from each 40 Gabor feature maps to obtain a 3340-dimensional feature vector, as shown in the right sub-figure of Fig. 7;
Use 64UniSample to obtain a 2560-dimensional feature vector, as shown in the left sub-figure of Fig. 7.

Finally, we use PCA to reduce the extracted feature dimension before we train the predictor. The results of the appearance-based shallow predictors for all the data are shown in Table VI.

V FBP Evaluation via Deep Predictor

We evaluate three recently proposed CNN models with different structures for FBP, including AlexNet [41], ResNet-18 [42] and ResNeXt-50 [43]

. All these CNN models are trained by initializing weights using networks pre-trained on the ImageNet dataset. The evaluation of were performed under two different experiment settings as following:

The models were trained and tested using 5-fold cross validation, which means each fold containing $20 %$ samples (1100 images). The accuracy of each fold and the average of all the 5 fold are shown in Table VII.
The models were trained using $60 %$ samples (3300 images), and tested with the rest $40 %$ (2200 images). The results are shown in Table VIII.

The results illustrates that the deepest CNN-based ResNeXt-50 model [43] obtains the best performance comparing to the ResNet-18 and AlexNet in both the experiment setting. It can be observed that all the deep CNN model are superior to the shallow predictor with hand-crafted geometric feature in Table V or appearance feature in Table VI. It indicates the effectiveness and powerfulness of the end-to-end feature learning deep model for FBP.

Comparing the results of Table VII and Table VIII, we also find that the accuracy of all the 5-fold cross validation is slightly higher than the results of the split of $60 %$ training and $40 %$ testing. One of the reasons may be due to the amounts and diversity of the training samples, since the 5-fold cross validation use $80 %$ samples to train the models. This observation indicates that the data augmentation techniques may further improve the performance of the deep FBP model, which merits exploring in the future.

Vi Conclusion

In this paper, we introduce 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). Benchmark analysis have been made for the beauty scores and landmarks in SCUT-FBP5500, and the visualization of the data shows the statistical properties of the dataset. Since the SCUT-FBP5500 is designed for multi-paradigm, it can be adapted to different FBP models for different tasks, like appearance-based or shape-based model for facial beauty classification/regression/ranking. We evaluated the SCUT-FBP5500 using different combinations of feature and predictor, and deep learning models, where the results indicates the reliability of the dataset.

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

I Know How You Feel: Emotion Recognition with Facial Landmarks

InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild

Lightweight Facial Attractiveness Prediction Using Dual Label Distribution

New Benchmark for Household Garbage Image Recognition

Label distribution based facial attractiveness computation by deep residual learning

Handling Missing Annotations in Supervised Learning Data

Unsupervised inference approach to facial attractiveness

I Introduction