Efficient Facial Attractiveness Prediction Using Dual Label Distribution and Lightweight Design

Основные понятия

A novel end-to-end facial attractiveness prediction approach that integrates dual label distribution and lightweight design to achieve promising results with greatly decreased parameters and computations.

Аннотация

The paper presents a novel end-to-end facial attractiveness prediction (FAP) approach that integrates dual label distribution and lightweight design. The key highlights are:

1. Dual Label Distribution:

• The manual ratings, attractiveness score, and standard deviation are aggregated explicitly to construct a dual-label distribution, including the attractiveness distribution and the rating distribution.
• The attractiveness distribution is generated using the Laplace distribution, while the rating distribution is derived directly from the manual ratings.
• The dual-label distribution is designed to make the best use of the dataset.

2. Joint Learning Framework:

• The dual-label distribution and the attractiveness score are jointly optimized under three learning modules: attractiveness distribution learning, rating distribution learning, and score regression learning.
• The joint learning framework aims to leverage the complementary information in the dual-label distribution to enhance the prediction performance.

3. Lightweight Design:

• The data preprocessing and augmentation are simplified to a minimum.
• MobileNetV2 is selected as the backbone to balance performance and efficiency.

Extensive experiments on two benchmark datasets demonstrate that the proposed approach achieves state-of-the-art or comparable results while greatly reducing the number of parameters and computations compared to previous methods. Ablation studies and visualization further validate the effectiveness of the delicately designed learning modules and the model's ability to perceive facial attractiveness.

Статистика

The average attractiveness score ranges from 1 to 5.
The standard deviation of the attractiveness scores is provided in the dataset.
The number of manual ratings for each image is also available.

Цитаты

"Facial attractiveness plays a significant role in daily life [1], [2]. It is a complex and multifactorial concept, devoting researchers from diverse disciplines to decrypting its mysteries [3]."
"With the emergence of deep learning, convolutional neural networks (CNNs) [14]–[16] have been applied to FAP. Due to their powerful nonlinearity, CNN-based methods are able to learn hierarchical aesthetic representations thus boosting the performance, but their large-scale models lack flexibility."

Ключевые выводы из

Lightweight Facial Attractiveness Prediction Using Dual Label Distribution

by Shu Liu,Enqu... в arxiv.org 04-25-2024

https://arxiv.org/pdf/2212.01742.pdf

Дополнительные вопросы

How can the proposed approach be extended to handle personalized facial attractiveness perception beyond the universal preference

The proposed approach can be extended to handle personalized facial attractiveness perception by incorporating additional features or data sources that capture individual preferences. One way to achieve this is by integrating user feedback or preferences into the model training process. By collecting data on individual preferences or ratings from users, the model can learn to adapt its predictions based on specific user profiles. This personalized approach can enhance the accuracy of facial attractiveness predictions by taking into account the diverse and subjective nature of beauty perceptions.

What are the potential limitations of the Laplace distribution in modeling the attractiveness distribution, and how can alternative probability distributions be explored to further improve the performance

While the Laplace distribution has shown effectiveness in modeling the attractiveness distribution, it may have limitations in capturing more complex or nuanced patterns in the data. One potential limitation is its assumption of symmetry around the mean, which may not always hold true in real-world datasets where attractiveness scores can be skewed or exhibit asymmetry. To address this limitation, alternative probability distributions such as the Beta distribution or the Gamma distribution can be explored. These distributions offer more flexibility in modeling skewed or non-symmetric data, allowing for a better fit to the underlying attractiveness distribution and potentially improving the model's performance.

Given the success of the lightweight design in FAP, how can the proposed framework be adapted to other computer vision tasks to achieve a balance between performance and efficiency

The success of the lightweight design in FAP can be leveraged to adapt the proposed framework to other computer vision tasks by focusing on efficiency and performance optimization. One way to achieve this is by selecting appropriate lightweight backbones or network architectures that are tailored to the specific task requirements. Additionally, the joint learning framework can be applied to tasks such as facial expression recognition, age estimation, or object detection, where balancing performance and efficiency is crucial. By customizing the learning modules and loss functions to suit the task at hand, the proposed framework can be extended to a variety of computer vision applications while maintaining a lightweight and efficient design.