A comprehensive review of facial beauty prediction using deep learning techniques

Facial beauty prediction (FBP) is an emerging area of artificial intelligence (AI) that focuses on the development of models that analyze facial features to assess beauty, based on human perception. This task is particularly challenging due to the subjective nature of beauty and limited resources. Deep learning methods have proven their exceptional ability to capture complex features and are therefore well suited for FBP tasks. This paper reviews recent advances in FBP, focusing on deep learning techniques and benchmark datasets. A proposed taxonomy organizes the main methods according to their design and applications, and comparative analyzes highlight trends and the performance of different models. Finally, the study outlines future research directions to drive progress in this evolving field.

• facial beauty prediction
• facial attractiveness
• facial analysis
• benchmark dataset

• Face recognition and analysis
• Evolutionary Psychology and Human Behavior
• Advanced Neural Network Applications
• facial beauty prediction
• facial attractiveness
• facial analysis
• benchmark dataset
• Computer Vision and Pattern Recognition
• Experimental and Cognitive Psychology
• Computer Science
• Psychology