Abstract
This paper presents a 3D-aware face clustering methodology that robustly groups unlabeled face images by identity under challenging conditions of pose variation, facial expression, and partial occlusion. The proposed approach integrates 2D deep embeddings with 3D geometric features extracted from reconstructed facial meshes, leveraging both photometric and structural information. Preprocessing includes grayscale normalization, landmark-based alignment, and contrast enhancement. 3D face models are generated using a 3D Morphable Model (3DMM) and optionally refined through neural rendering to improve shape fidelity. From these reconstructions, we extract interpretable 3D descriptors-PCA shape coefficients, geodesic distances, and curvature histograms - that complement embeddings from ArcFace and FaceNet. Clustering is performed using a two-stage hybrid algorithm: DBSCAN for outlier removal followed by K-Means++ with a fused distance metric combining cosine and Mahalanobis distances. Experimental results demonstrate that the proposed method significantly outperforms 2D-only and 3D-only baselines in terms of Silhouette Score, Adjusted Rand Index (ARI), and Purity. The findings confirm that fusing 2D and 3D modalities yields semantically consistent and pose-invariant identity clusters, establishing a strong foundation for face analysis in unconstrained environments.