Facial beauty prediction fusing transfer learning and broad learning system

2026-03-13

AI Summary

Facial aesthetic prediction faces significant challenges due to data scarcity, high appearance variability, and the subjective nature of beauty, often leading to model overfitting and poor generalization. To address these issues, this work proposes a novel approach that integrates transfer learning with Broad Learning System (BLS), introducing EfficientNet-based BLS (E-BLS) and its enhanced variant with residual connections (ER-BLS). By optimizing the feature propagation architecture, the proposed models achieve improved predictive performance while maintaining computational efficiency during training. Extensive experiments demonstrate that both E-BLS and ER-BLS significantly outperform existing convolutional neural network (CNN) and BLS baselines across multiple evaluation metrics, thereby validating their effectiveness and strong generalization capability in facial aesthetic assessment.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Facial beauty prediction (FBP) is an important and challenging problem in the fields of computer vision and machine learning. Not only it is easily prone to overfitting due to the lack of large-scale and effective data, but also difficult to quickly build robust and effective facial beauty evaluation models because of the variability of facial appearance and the complexity of human perception. Transfer Learning can be able to reduce the dependence on large amounts of data as well as avoid overfitting problems. Broad learning system (BLS) can be capable of quickly completing models building and training. For this purpose, Transfer Learning was fused with BLS for FBP in this paper. Firstly, a feature extractor is constructed by way of CNNs models based on transfer learning for facial feature extraction, in which EfficientNets are used in this paper, and the fused features of facial beauty extracted are transferred to BLS for FBP, called E-BLS. Secondly, on the basis of E-BLS, a connection layer is designed to connect the feature extractor and BLS, called ER-BLS. Finally, experimental results show that, compared with the previous BLS and CNNs methods existed, the accuracy of FBP was improved by E-BLS and ER-BLS, demonstrating the effectiveness and superiority of the method presented, which can also be widely used in pattern recognition, object detection and image classification.

Problem

• Facial beauty prediction
• overfitting
• data scarcity
• human perception complexity
• robust model building

Innovation

• Transfer Learning
• Broad Learning System
• Facial Beauty Prediction
• EfficientNet
• Feature Fusion