A Comprehensive Review of Facial Beauty Prediction Using Multi-task Learning and Facial Attributes

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Beauty multi-task prediction from facial attributes is a multidisciplinary challenge at the intersection of computer vision, machine learning, and psychology. Despite the centrality of beauty in human perception, its subjective nature—shaped by individual, social, and cultural influences—complicates its computational modeling. This review addresses the pressing need to develop robust and fair predictive models for facial beauty assessments by leveraging deep learning techniques. Using facial attributes such as symmetry, skin complexion, and hairstyle, we explore how these features influence perceptions of attractiveness. The study adopts advanced computational methodologies, including convolutional neural networks and multi-task learning frameworks, to capture nuanced facial cues. A comprehensive analysis of publicly available datasets reveals critical gaps in diversity, biases, and ground truth annotation for training effective models. We further examine the methodological challenges in defining and measuring beauty, such as data imbalances and algorithmic fairness. By synthesizing insights from psychology and machine learning, this work highlights the potential of interdisciplinary approaches to enhance the reliability and inclusivity of automated beauty prediction systems.