Face recognition algorithms are becoming increasingly capable of extracting identity information from poor images. But new research into low-resolution face recognition highlights a stubborn operational problem: better algorithms can recover more from degraded imagery, but only when useful identity information remains. Researchers at Johns Hopkins University tested a new approach to recognizing faces affected by conditions common in surveillance imagery, including blur, occlusion, poor contrast,