High precision face recognition based on unmanned aerial vehicle (UAV) images plays an important role in emergency rescue, suspect tracking, and other scenes. Deep learning convolutional neural network is widely used in the field of target detection and recognition because of its high accuracy and less human interference, which can be well applied to UAV image face recognition tasks. This paper explores the use of convolution networks for high-precision face recognition in UAV application scenarios, uses the improved YOLOv3(you only look once) for face detection of UAV images, and inputs the prediction boxes into the classic Facenet network to determine the target identity. Through experiments, this paper compares the detection effect of the improved YOLOv3, the original YOLOv3, and the MTCNN (multi-task convolutional neural network), and also compares the face recognition effect of the three models combined with Facenet. The experimental results show that: 1) compared with the original YOLOv3, the improved YOLOv3 improves the accuracy and recall rate, reduces the number of model parameters; besides, the phenomenon of missing and wrong detection of the improved YOLOv3 for UAV image is less than that of the original YOLOv3; moreover, the AP (average precision) of improved YOLOv3 is 9.49% higher than that of MTCNN, and the detection speed is about 3 times of MTCNN; 2) compared with the original YOLOv3+Facenet and MTCNN+Facenet, the improved YOLOv3+Facenet has stronger ability to distinguish faces and higher accuracy, and has stronger robustness to occlusion and blur.
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