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

Lebedeva, Irina; Guo, Yi; Ying, Fangli

doi:10.1007/s00521-021-06535-0

Cited by 9 publications

(4 citation statements)

References 43 publications

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“…A multi-task CNN called HMTNet was proposed in the study [20] that can predict the beauty score of a facial image besides race and gender utilizing SCUT-FBP and SCUT-FBP5500 datasets. Furthermore, a new dataset was proposed in the study [21], and the knowledge of DenseNet, Xception pre-trained on ImageNet, and VGG16 that pretrained on VGGFace2 dataset was transferred to find the best model for FBP. To make deep neural networks deployable on compact hardware, Saeed et al [22] built a light-deep CNN for assessing the attractiveness of facial images from scratch called FIAC-Net and evaluated the presented network on SUT-FBP, SUT-FBP5500, and CelebA datasets.…”

Section: Related Workmentioning

confidence: 99%

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An Ensemble DCNNs-Based Regression Model for Automatic Facial Beauty Prediction and Analyzation

Saeed¹,

Abdulazeez²,

Ibrahim³

2023

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One of the most effective social aspects of the human face is its attractiveness. Automatic facial beauty prediction (FBP) is an emerging research area that has gained much interest recently. However, identifying the significant facial traits and attributes that can contribute to the process of beauty attractiveness estimation is one of the main challenges in this research area. Furthermore, learning the beauty pattern from a relatively small, imbalanced dataset is another concern that needs to be addressed. This research proposes an ensemblebased regression model that integrates judgments made by three various DCNNs, each with a different structure representation. The proposed method efficiently predicts the beauty score by leveraging the strengths of each network as a complementary data source, and it draws attention to the most important beauty-related face features through the Gradientweighted Class Activation Mapping (Grad-CAM). The findings are promising, demonstrating the efficiency of fusing the decision of multiple predictors of the proposed ensemble DCNNs regression models that is significantly consistent with the ground truth of the employed datasets (SCUT-FBP, SCUT-FBP5500, and ME Beauty). Moreover, it can assist in comprehending the relationship between facial characteristics and the impression of attractiveness.

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Section: Related Workmentioning

confidence: 99%

“…•MEBeauty [21] is a multi-ethnic of 2550 facial images in-the-wild dataset that was created more recently. It includes 1250 male and 1300 female faces of various ages.…”

Section: Datasetmentioning

confidence: 99%

An Ensemble DCNNs-Based Regression Model for Automatic Facial Beauty Prediction and Analyzation

Saeed¹,

Abdulazeez²,

Ibrahim³

2023

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“…They devised a loss function called "Smooth Huber Loss". Similarly, Lebedeva et al [33] employed the Huber loss function to assess various known pretrained CNNs on their recently introduced multi-ethnic dataset, as they anticipated a substantial number of outlier faces. However, the choice of the delta parameter in Huber loss can significantly impact the optimization process and ultimately the prediction accuracy, which may require additional hyperparameter tuning.…”

Section: Related Workmentioning

confidence: 99%

“…MEBeauty is a recently produced multi-ethnic collection comprising 2550 in-the-wild facial photos presented by Lebedeva et al [33] in 2021. It has the facial images of 1250 men and 1300 women, comprising six ethnic groups, namely Black, Asian, Caucasian, Hispanic, Indian, and Middle Eastern people, with various ages, poses, backgrounds, and expressions represented.…”

Section: Mebeautymentioning

confidence: 99%

Automatic Facial Aesthetic Prediction Based on Deep Learning with Loss Ensembles

Saeed,

Abdulazeez,

Ibrahim

2023

Applied Sciences

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Deep data-driven methodologies have significantly enhanced the automatic facial beauty prediction (FBP), particularly convolutional neural networks (CNNs). However, despite its wide utilization in classification-based applications, the adoption of CNN in regression research is still constrained. In addition, biases in beauty scores assigned to facial images, such as preferences for specific, ethnicities, or age groups, present challenges to the effective generalization of models, which may not be appropriately addressed within conventional individual loss functions. Furthermore, regression problems commonly employ L2 loss to measure error rate, and this function is sensitive to outliers, making it difficult to generalize depending on the number of outliers in the training phase. Meanwhile, L1 loss is another regression-loss function that penalizes errors linearly and is less sensitive to outliers. The Log-cosh loss function is a flexible and robust loss function for regression problems. It provides a good compromise between the L1 and L2 loss functions. The Ensemble of multiple loss functions has been proven to improve the performance of deep-learning models in various tasks. In this work, we proposed to ensemble three regression-loss functions, namely L1, L2, and Log-cosh, and subsequently averaging them to create a new composite cost function. This strategy capitalizes on the unique traits of each loss function, constructing a unified framework that harmonizes outlier tolerance, precision, and adaptability. The proposed loss function’s effectiveness was demonstrated by incorporating it with three pretrained CNNs (AlexNet, VGG16-Net, and FIAC-Net) and evaluating it based on three FBP benchmarks (SCUT-FBP, SCUT-FBP5500, and MEBeauty). Integrating FIAC-Net with the proposed loss function yields remarkable outcomes across datasets due to its pretrained task of facial-attractiveness classification. The efficacy is evident in managing uncertain noise distributions, resulting in a strong correlation between machine- and human-rated aesthetic scores, along with low error rates.

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Jointly learning distribution and expectation in a unified framework for facial age and attractiveness estimation

Gao

2023

Neural Comput & Applic

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

Cited by 9 publications

References 43 publications

An Ensemble DCNNs-Based Regression Model for Automatic Facial Beauty Prediction and Analyzation

An Ensemble DCNNs-Based Regression Model for Automatic Facial Beauty Prediction and Analyzation

Automatic Facial Aesthetic Prediction Based on Deep Learning with Loss Ensembles

Jointly learning distribution and expectation in a unified framework for facial age and attractiveness estimation

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