Improvement of Facial Beauty Prediction Using Artificial Human Faces Generated by Generative Adversarial Network

Laurinavičius, Donatas; Maskeliūnas, Rytis; Damaševičius, Robertas

doi:10.1007/s12559-023-10117-8

Cited by 4 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Beauty prediction involves the application of computational methods to assess and quantify perceived beauty, with a focus on understanding the intricate factors that contribute to this subjective notion [3]. This evolution towards a more analytical and data-driven approach to beauty has paved the way for the incorporation of advanced technologies like deep learning [4].…”

Section: A Introductionmentioning

confidence: 99%

Facial Beauty Prediction Based on Deep Learning: A Review

Arabo,

Abdulazeez

2024

ijcs

View full text Add to dashboard Cite

This review delves into Facial Beauty Prediction (FBP) using deep learning, specifically focusing on convolutional neural networks (CNNs). It synthesizes recent advancements in the field, examining diverse methodologies and key datasets like SCUT-FBP and SCUT-FBP5500. The review identifies trends in FBP research, including the evolution of deep learning models and the challenges of dataset biases and cultural specificity. The paper concludes by emphasizing the need for more inclusive and balanced datasets and suggests future research directions to enhance model fairness and address ethical implications.

show abstract

Section: A Introductionmentioning

confidence: 99%

Facial Beauty Prediction Based on Deep Learning: A Review

Arabo,

Abdulazeez

2024

ijcs

View full text Add to dashboard Cite

show abstract

“…Solving the aforementioned issue has become a popular subject in the field of FBP research. At present, some progress has been made in FBP research [1][2][3][4][5][6][7]. In [1], a novel personalized FBP approach based on meta-learning was designed to apply in some small databases.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, an instance-level dynamic exponential loss function was designed to adjust the optimization objectives of the model dynamically. In [7], a novel method was proposed to improve the facial beauty feature extraction ability of CNNs, in which generative adversarial networks (GAN) were used to generate facial data.…”

Section: Introductionmentioning

confidence: 99%

Facial Beauty Prediction Combined with Multi-Task Learning of Adaptive Sharing Policy and Attentional Feature Fusion

Gan,

Luo,

Xiong

et al. 2023

Electronics

View full text Add to dashboard Cite

Facial beauty prediction (FBP) is a leading research subject in the field of artificial intelligence (AI), in which computers make facial beauty judgments and predictions similar to those of humans. At present, the methods are mainly based on deep neural networks. However, there still exist some problems such as insufficient label information and overfitting. Multi-task learning uses label information from multiple databases, which increases the utilization of label information and enhances the feature extraction ability of the network. Attentional feature fusion (AFF) combines semantic information and introduces an attention mechanism to reduce the risk of overfitting. In this study, the multi-task learning of an adaptive sharing policy combined with AFF is presented based on the adaptive sharing (AdaShare) network in FBP. First, an adaptive sharing policy is added to multi-task learning with ResNet18 as the backbone network. Second, the AFF is introduced at the short skip connections of the network. The proposed method improves the accuracy of FBP by solving the problems of insufficient label information and overfitting issues. The experimental results based on the large-scale Asia facial beauty database (LSAFBD) and SCUT-FBP5500 databases show that the proposed method outperforms the single-database single-task baseline and can be applied extensively in image classification and other fields.

show abstract