In the previous chapter, we introduced 3D face reconstruction techniques, which can accurately recover 3D facial models from single images. This approach is particularly suitable for tasks such as facial beauty analysis, where annotated data are scarce and high-quality samples are difficult to obtain, thus laying the foundation for 3D aesthetics research. Facial beauty analysis (FBA) has broad applications in social media, cosmetics, and healthcare, yet most existing methods rely solely on 2D images and neglect the essential depth information. In this chapter, we present the reconstruction-based aesthetic feature acquisition method, which transfers handcrafted aesthetic feature analysis to 3D models. We further apply feature selection to retain key 3D geometric features and construct a comprehensive 2D–3D feature set. Comparative experiments with classical geometric-feature-based models demonstrate the significant advantages of 3D features, offering improved robustness to pose and expression variations. This chapter highlights the necessity and effectiveness of incorporating 3D features into facial beauty research.