An Uncertainty-Aware and Explainable Deep Learning Model for Facial Beauty Prediction

The automated prediction of facial beauty is a challenging computer vision task due to the inherent subjectivity and complexity of human perception. Traditional models typically predict a single, deterministic score, failing to capture the ambiguity and variance in human judgments. This paper introduces a novel probabilistic approach that frames facial beauty prediction as an uncertainty estimation problem. We propose a dual-head deep convolutional neural network (CNN) that predicts not only the mean beauty score but also the underlying uncertainty (variance) of its prediction. This is achieved using a state-of-the-art EfficientNetV2-S backbone and a custom Gaussian Negative Log-Likelihood (NLL) loss function, which encourages the model to learn its own confidence. By modeling predictions as a probability distribution, our approach provides a richer, more informative output. Furthermore, we leverage Gradient-weighted Class Activation Mapping (Grad-CAM) to provide visual explanations, highlighting the facial regions most influential to the model's predictions. Experiments conducted on the FBP5500 dataset across both 5-fold cross-validation and a fixed 60%-40% train-test split. Our model achieves new state-of-the-art performance, with a mean Pearson Correlation of 0.9277, surpassing existing single-model and complex ensemble methods. The results demonstrate that our probabilistic and explainable model achieves strong quantitative performance while simultaneously offering a valuable measure of prediction uncertainty, paving the way for more robust and interpretable models in subjective assessment tasks. The code is available at https://github.com/DjameleddineBoukhari/XAI-FBP