AerialFace: A Light Weight Framework for Unmanned Aerial Vehicle Face Recognition

Abstract

— Unmanned Aerial Vehicle (UAV) are widely applied in multiple fields due to their simple structure and high flexibility. Applying facial recognition technology to UAV can improve their intelligence and diversity of application scenarios. However, UAV face recognition is often hindered by low resolution face, resulting in low accuracy. To alleviate these issues, we propose an efficient face recognition framework named AerialFace. Firstly, we utilize the Residual SRGAN (ResSRGAN) model to enhance image quality and generate highresolution face images. Then, we propose Semantic-improved MobileFaceNet (SeMFNet) to relieve the impact of complex backgrounds. Finally, we leverage two pruning algorithms for face detection and recognition models, respectively. It can reduce their parameters to meet the deployment requirements of the algorithm on UAVs. Furthermore, we apply our AerialFace on a UAV face dataset and employ it on an edge computing device. Extensive experiments demonstrate that our approach can effectively improve UAV face recognition accuracy and have real-time performance in embedded UAV devices.

姚亮

Master of Engineering

My research interests include Artificial Intelligence, UAV Object Detection and Feature Disentanglement.