The present study is the first empirical investigation of the relationship between human deepfake detection performance (DDP) and individuals’ face identity processing (FIP) ability. Using videos from the Deepfake Detection Challenge, we investigated DDP in two unique observer groups: Super-Recognizers (SRs) and “normal” officers from within the 18,000 members of the Berlin Police. SRs were identified either via previously proposed lab-based procedures or the only existing tool for SR identification involving increasingly challenging authentic forensic material: the Berlin Test For Super-Recognizer Identification (beSure). Participants judged either pairs of videos or single videos in a two-alternative forced-choice (2AFC) decision setting (that is, which of the pair or whether a single video was a deepfake or not). We explored speed–accuracy tradeoffs and compared DDP between lab-identified SRs and non-SRs and police officers as a function of their independently measured FIP ability. Interestingly, we found no relationship between DDP and FIP ability. Further work using static deepfakes created with current state-of-the-art generative models is needed to determine the value of SR deployment for deepfake detection in law enforcement.