Unmanned Aerial Vehicles (UAVs) equipped with real-time face recognition systems play a crucial role in security and surveillance applications, particularly for intruder detection in high-security environments. However, the efficiency of these systems heavily depends on the performance of the face detection algorithm, which serves as the first stage in the face recognition pipeline. This paper evaluates the impact of three state-of-the-art face detection algorithms—UWS-YOLO, YOLOv7, and RetinaFace—on the real-time performance of a UAV-based face recognition system. Experimental results show that UWS-YOLO achieves the best balance between speed and accuracy, with an inference time of 12.5 ms, an Intruder Detector AI processing time of 26.8 ms, and an overall face recognition pipeline execution time of 48.3 ms, outperforming YOLOv7 (59.3 ms) and RetinaFace (67.9 ms). These findings highlight the importance of minimising face detection latency to ensure real-time UAV-based surveillance operations.