SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction (1801.06345v1)

Abstract: Facial beauty prediction (FBP) is a significant visual recognition problem to make assessment of facial attractiveness that is consistent to human perception. To tackle this problem, various data-driven models, especially state-of-the-art deep learning techniques, were introduced, and benchmark dataset become one of the essential elements to achieve FBP. Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms. However, most of FBP benchmark datasets were built under specific computation constrains, which limits the performance and flexibility of the computational model trained on the dataset. In this paper, we argue that FBP is a multi-paradigm computation problem, and propose 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), which allows different computational models with different FBP paradigms, such as appearance-based/shape-based facial beauty classification/regression model for male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP using different combinations of feature and predictor, and various deep learning methods. The results indicates the improvement of FBP and the potential applications based on the SCUT-FBP5500.

• The paper presents the SCUT-FBP5500 dataset that standardizes facial beauty prediction tasks using classification, regression, and ranking paradigms.
• It evaluates both shallow and deep learning models, with ResNeXt-50 delivering the highest prediction accuracy on complex facial beauty assessments.
• The benchmark enables cross-cultural analyses and supports future research in personalized beauty prediction and automated image enhancement.

Multi-Paradigm Approaches to Facial Beauty Prediction with SCUT-FBP5500

The paper "SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction" presents a notable contribution to the domain of facial beauty prediction (FBP), primarily through the introduction of the SCUT-FBP5500 dataset. This dataset is designed to foster research in FBP and serve as a standard benchmark to evaluate different modeling methodologies, including classification, regression, and ranking. This piece of work is pivotal in highlighting the multi-paradigm nature of FBP, which has often been constrained by the limitations of prior datasets.

Key Features of SCUT-FBP5500

The SCUT-FBP5500 dataset comprises 5500 frontal face images, capturing a diverse set of facial characteristics such as gender (male/female), ethnicity (Asian/Caucasian), and age. These images are rated with beauty scores ranging from 1 to 5, assigned by 60 volunteers, ensuring a robust variety of perceptions. In comparison to other datasets in the field, SCUT-FBP5500 stands out due to its diversity and scale, allowing for more generalizable and flexible FBP model development. The dataset also includes facial landmarks, which are crucial for geometry-based FBP analysis.

Evaluation and Benchmarking

The authors systematically evaluate the SCUT-FBP5500 dataset using several modeling techniques:

1. Shallow Modeling Techniques: Geometric and appearance-based features were extracted and used in tandem with linear regression (LR), Gaussian regression (GR), and support vector regression (SVR) models. The performance metrics reveal the SCUT-FBP5500's utility in providing a reliable baseline for these traditional methods.
2. Deep Learning Approaches: The paper also assesses state-of-the-art neural network architectures such as AlexNet, ResNet, and ResNeXt. ResNeXt-50 achieved the highest prediction accuracy, demonstrating the efficacy of deep learning models on the dataset. This finding aligns with contemporary literature that deep models often outperform shallow counterparts in complex tasks such as FBP.

Implications and Future Directions

The dataset's comprehensive nature enables various research questions and applications in FBP, such as cross-cultural analysis, personalized beauty prediction, and automation in image enhancement applications. The diversity offered by SCUT-FBP5500 supports the examination of cultural influences on beauty standards and the development of models that can adapt to varying subjective perceptions.

Moreover, the dataset facilitates benchmarking across different paradigms, enabling a more holistic assessment of model performance. Moving forward, researchers could leverage augmentation techniques to further boost model performance or explore the applicability of transfer learning and domain adaptation techniques to enhance the FBP model's generalizability.

Conclusion

The introduction of SCUT-FBP5500 provides a foundational platform for advancing research in facial beauty prediction. Its diverse and large-scale nature addresses significant limitations of previous datasets, offering a comprehensive resource for developing and evaluating multi-paradigm computational models. This work not only promotes methodological diversity but also paves the way for future inquiries into the cultural and subjective dimensions of facial beauty assessment, setting a benchmark for subsequent explorations in both theoretical and applied machine learning domains.