Pose Invariant Face Recognition

Pose-invariant face recognition aims to develop systems that accurately identify individuals regardless of head pose variations. Current research focuses on improving feature extraction through attention mechanisms operating in hierarchical feature spaces, rather than relying solely on image-space transformations like frontalization. These advancements leverage techniques like semantic segmentation feature distillation and attention modules to enhance robustness and efficiency, achieving significant improvements in accuracy while reducing computational demands. This work has significant implications for improving the reliability and scalability of face recognition technologies across diverse applications.