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Can We Extract 3D Biometrics from 2D Images for Facial Beauty Analysis?

Geometric features are important traits in facial beauty analysis due to its clear definition and coherence to our intuition. Existing works often extract these features from 2D face images captured in constrained environment. With the recent development of monocular reconstruction algorithms, we wonder if it is possible to reconstruct 3D faces from 2D images and extract 3D biometrics instead of extracting features from the 2D images directly. If the features are robust, a large amount of in-the-wild face images will be available for facial beauty analysis. In this paper, we design experiments to evaluate the precision and robustness of 2D-3D geometric features. 3D faces in the BJUT-3D database were taken as the ground truth. Based on these 3D faces, we generated 2D images with different poses and fed them to a deep neural network to obtain the reconstructed 3D faces. Ratio and angle features were extracted from the ground truth 3D faces, the generated 2D faces, and the reconstructed 3D faces, respectively. The results show that the 2D-3D geometric features were more robust to pose variations compared with those of 2D images; ambiguities exist in monocular reconstruction, but the error is smaller than the pairwise individual differences.