From Eigenfaces to Fine-Grained Attention: The Evolution of Face Recognition and the Identical-Twin Frontier

Automatic face recognition (FR) has progressed from hand-engineered subspace projections in the early 1990s to deep neural embeddings that now exceed human accuracy on standard verification benchmarks. Yet a single, biologically extreme test case exposes the limits of this progress: pairs of identical (monozygotic) twins, whose facial appearance is genetically near-indistinguishable. This survey traces five decades of FR method families—subspace and geometric methods, local texture descriptors, deep metric embeddings, margin-based angular losses, and the current era of transformers, quality-adaptive margins, and synthetic data—and then reframes that history through the lens of twin discrimination. We argue that identical twins constitute a natural stress test that isolates the ceiling of two-dimensional, appearance-based recognition: modern systems that report over 99.8% accuracy on mainstream benchmarks drop to roughly 88–92% on identical-twin verification. We organize the literature into a taxonomy along a twin-discrimination axis (appearance-only vs. multimodal/3D vs. mark-based and fine-grained methods), catalogue the specialized twin datasets that anchor this research (ND-TWINS-2009-2010, the Twins Days 3D challenge, and related multibiometric collections), and compile reported accuracies with their protocol caveats. We close with the open problems that the twin frontier makes unavoidable: fine-grained discriminative learning, multimodal and mark-based fusion, demographic fairness, presentation-attack and morphing robustness, and the regulatory pressures of the deployed 2026 landscape.